Literature DB >> 33354596

Formulating multi diseases dataset for identifying, triaging and prioritizing patients to multi medical emergency levels: Simulated dataset accompanied with codes.

Omar H Salman1, Mohammed I Aal-Nouman2, Zahraa K Taha1, Muntadher Q Alsabah3, Yaseein S Hussein4, Zahraa A Abdelkareem5.   

Abstract

This paper provides simulated datasets for triaging and prioritizing patients that are essentially required to support multi emergency levels. To this end, four types of input signals are presented, namely, electrocardiogram (ECG), blood pressure, and oxygen saturation (SpO2), where the latter is text. To obtain the aforementioned signals, the PhysioNet online library [1], is used, which is considered as one of the most reliable and relevant libraries in the healthcare services and bioinformatics sciences. In particular, this library contains collections of several databases and signals, where some of these signals are related to ECG, blood pressure, and SpO2 sensor. The simulated datasets, which are accompanied by codes, are presented in this paper. The contributions of our work, which are related to the presented dataset, can be summarized as follow. (1) The presented dataset is considered as an essential feature that is extracted from the signal records. Specifically, the dataset includes medical vital features such as: QRS width; ST elevation; peaks number; cycle interval from ECG signal; SpO2 level from SpO2 signal; high blood (systolic) pressure value; and low-pressure (diastolic) value from blood pressure signal. These essential features have been extracted based on our machine learning algorithms. In addition, new medical features are added based on medical doctors' recommendations, which are given as text-inputs, e.g., chest pain, shortness of breath, palpitation, and whether the patient at rest or not. All these features are considered to be significant symptoms for many diseases such as: heart attack or stroke; sleep apnea; heart failure; arrhythmia; and blood pressure chronic diseases. (2) The formulated dataset is considered in the doctor diagnostic procedures for identifying the patients' emergency level. (3) In the PhysioNet online library [1], the ECG, blood pressure, and SpO2 have been represented as signals. In contrast, we use some signal processing techniques to re-present the dataset by numeric values, which enable us to extract the essential features of the dataset in Excel sheet representations. (4) The dataset is re-organized and re-formatted to be presented in a useful structure feasible format. Specifically, the dataset is re-presented in terms of tables to illustrate the patient's profile and the type of diseases. (5) The presented dataset is utilized in the evaluation of medical monitoring and healthcare provisioning systems [2]. (6) Some simulated codes for feature extractions are also provided in this paper.
© 2020 The Author(s).

Entities:  

Keywords:  Blood pressure; E- health; ECG; Healthcare; Machine learning; Sensor; SpO2; Telemedicine

Year:  2020        PMID： 33354596      PMCID： PMC7744952          DOI： 10.1016/j.dib.2020.106576

Source DB:  PubMed          Journal:  Data Brief        ISSN： 2352-3409


Specifications Table

Value of the Data

The effectiveness of the presented dataset can be summarized as follows: The presented dataset contains some of the essential features of patients. In particular, these patients' features can be considered as significant symptoms indicators of many diseases such as: (a) Heart attack or stroke; (b) Sleep apnea; (d) Heart failure; (e) Arrhythmia; and (f) Blood pressure chronic diseases [3,4]. In addition, from the doctor diagnostics procedure point of view, these features can be considered as essential indicators of other sicknesses such as: (a) Adrenal gland tumours; (b) Thyroid problem; (c) Dementia; (d) Kidney disease; and (e) Peripheral arterial disease. The presented dataset is very beneficial to the researchers in both academic and industrial sectors. In particular, the presented dataset would allow researchers to make a fast decision and reliable assessment for the patients at the emergency level [7]. Specifically, such kind of assessment can be effectively used to decide whether the patients need fast medical services or can wait for a certain period and then served. As such, healthcare institutes, e.g., hospitals, would be able to provide fast and more efficient healthcare, and thus, increase their productive services and manage their medical resources more effectively. The presented dataset involves heterogeneous sources, which contain some medical sensors. i.e., ECG, SpO2 and blood pressure and text-inputs [5,7]. Such a combination of datasets provides valuable insights to the researchers in both academic and industrial business sectors. This particularly allows the researchers to design smart and intelligent healthcare systems, which are essential for the currently deployed Internet of Things (IoT) applications [8]. The presented dataset is useful to a variety of research studies. For example, it can be used in triaging, classifying and prioritizing patients to multi emergency levels such as Risk, Urgent, Sick, Cold case and Normal.

Dataset description

The dataset presented in this paper includes ECG, blood pressure and SpO2 records and text-inputs. The dataset has been collected from PhysioNet databases [1]. However, the collected dataset is simulated, re-organized, re-structured in tables context to extract (1) some essential features from the signals, (2) database type, (3) signal record, (5) type of disease and (6) patients' profiles. All these details are presented in the attached appendixes with the following brief descriptions: Table 1 outlines the description of ECG databases and signals records along with all the patients' profiles. Moreover, a sample of the ECG signal is presented in Fig. 1.
Table 1

ECG databases and records that used in simulating algorithms.

DatabaseRecord (Signal)Record Description
Apnea-ECG data base (apnea-ecg)a01Male.Age: 51.Height: 175 (cm).Weight: 102 (Kg).60 seconds (1 minute) length.Data standard format.
Apnea-ECG data base (apnea-ecg)a03Male.Age: 54.Height: 168 (cm).Weight: 80 (Kg).60 seconds (1 min) length.Data standard format.
Apnea-ECG data base (apnea-ecg)b01Female.Age: 44.Height: 170 (cm).Weight: 63 (Kg).60 seconds (1 min) length.Data standard format.
Apnea-ECG data base (apnea-ecg)x15Male.Age: 63.Height: 179 (cm).Weight: 104 (Kg).60 seconds (1 min) length.Data standard format.
MIT-BIH Arrhythmia database (mitdb)In most records, the upper signal is a modified limb lead II (MLII), obtained by placing the electrodes on the chest. The lower signal is usually a modified lead V1 (occasionally V2 or V5, and in one instance V4); as for the upper signal, the electrodes are also placed on the chest. This configuration is routinely used by the BIH Arrhythmia Laboratory.Normal QRS complexes are usually prominent in the upper signal.
MIT-BIH Arrhythmia database (mitdb)100Male.Age: 69.60 seconds (1 min) length.Signal (V5).Data standard format.The patient uses these Medications: Aldomet and Indera.
MIT-BIH Arrhythmia database (mitdb)101Female.Age: 75.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses this Medication: Diapres.
MIT-BIH Arrhythmia database (mitdb)102Female.Age: 84.60 seconds (1 min) length.Signal (V5).Data standard format.The patient uses this Medication: Digoxin
MIT-BIH Arrhythmia database (mitdb)103Male.Age: Not Recorded.60 seconds (1 min) length.Signal (V5).Data standard format.The patient uses these Medications: Diapres and Xyloprim.
MIT-BIH Arrhythmia database (mitdb)105Female.Age: 73.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses these Medications: Digoxin, Nitropaste and Pronestyl.
MIT-BIH Arrhythmia database (mitdb)106Female.Age: 24.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses this Medication: Inderal.
MIT-BIH Arrhythmia database (mitdb)107Male.Age: 63.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses this Medication: Digoxin
MIT-BIH Arrhythmia database (mitdb)109Male.Age: 64.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses this Medication: Quinidine.
MIT-BIH Arrhythmia database (mitdb)111Female.Age: 47.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses these Medications: Digoxin and Lasix.
MIT-BIH Arrhythmia database (mitdb)114Female.Age: 72.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses this Medication: Digoxin
MIT-BIH Arrhythmia database (mitdb)115Female.Age: 39.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient does not use any Medication.
MIT-BIH Arrhythmia database (mitdb)116Male.Age: 68.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient does not use any Medication.
MIT-BIH Arrhythmia database (mitdb)118Male.Age: 69.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses these Medications: Digoxin and Norpace.
MIT-BIH Arrhythmia database (mitdb)119Female.Age: 51.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses this Medication: Pronestyl.
MIT-BIH Arrhythmia database (mitdb)121Female.Age: 83.60 seconds (1 min) length.Signal (MLII).Data standard format.The patient uses these Medications: Digoxin, Isordil and Nitropaste.
Long Term ST database (ltstdb)s20011Male.Age: 58.60 seconds (1 min) length.Signal (ML2).Data standard format.The diagnostic of the patient is: No Coronary artery disease.
Long Term ST database (ltstdb)s20051Female.Age: 87.60 seconds (1 min) length.Signal (ML2).Data standard format.The diagnostic of the patient is: Hypertension.
Long Term ST database (ltstdb)s20201Female.Age: 78.60 seconds (1 min) length.Signal (ML2).Data standard format.The diagnostic of the patient is: Syncope and seizure disorder.
Long Term ST database (ltstdb)s20272Male.Age: 61.60 seconds (1 min) length.Signal (ML2).Data standard format.The diagnostic of the patient is: Coronary artery disease.
The BIDMC Congestive Heart Failure DatabaseThis database includes long-term ECG recordings from 15 subjects (11 men, aged 22 to 71, and 4 women, aged 54 to 63) with severe congestive heart failure (NYHA class 3–4).This group of subjects was part of a larger study group receiving conventional medical therapy prior to receiving the oral inotropic agent, milrinone.ECG signals sampled at 250 samples per second with 12-bit resolution over a range of ±10 millivolts.
The BIDMC Congestive Heart Failure Databasechf01Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
The BIDMC Congestive Heart Failure Databasechf02Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
The BIDMC Congestive Heart Failure Databasechf03Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
The BIDMC Congestive Heart Failure Databasechf04Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
The BIDMC Congestive Heart Failure Databasechf05Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
The BIDMC Congestive Heart Failure Databasechf06Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1)
The BIDMC Congestive Heart Failure Databasechf07Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
The BIDMC Congestive Heart Failure Databasechf08Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
The BIDMC Congestive Heart Failure Databasechf09Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
The BIDMC Congestive Heart Failure Databasechf10Gender: not AvailableAge: Not available.60 seconds (1 min) length.Signal (ECG1).Data standard format.
Fig. 1

ECG Sample dataset for record a01 [1].

ECG databases and records that used in simulating algorithms. ECG Sample dataset for record a01 [1]. Table 2 shows the description of SpO2 database and signal records. In addition, SpO2 signal sample is showed in Fig. 2.
Table 2

SpO2 database and records that used in simulating algorithms.

DatabaseRecordsDescriptions for all the records
MIMIC Database Numerics032n, 033n, 037n, 039n, 041n, 048n, 052n, 054n, 055n, 208n, 209n, 210n, 211n, 212n, 213n, 214n, 215n, 216n, 218n, 219n, 220n, 221n, 224n, 225n, 226n, 230n, 231n, 232n, 233n, 235n, 237n, 240n, 241n, 242n, 243n, 245n, 248n, 252n, 253n, 254n, 255n, 259n, 260n, 262n, 264n, 267n, 268n, 224n, 225n and 269n.60 seconds (1 min) length.Time standard Format.Data standard format.MIMIC Database is called Numerics because these measurements are those that typically appear in numeric form on the ICU monitors' screens: heart rate, blood pressure (mean, systolic, diastolic), respiration rate, oxygen saturation, etc. Since the data-gathering protocol was designed to have minimal impact on patient monitoring or care, the selection of measured variables varies among these records, according to the requirements of the ICU staff for appropriate care of the patients in each case.The measurements in these records, sampled at intervals of 1.024 seconds (0.976563 Hz),
Fig. 2

SpO2 Sample dataset for record 032n [1].

SpO2 database and records that used in simulating algorithms. SpO2 Sample dataset for record 032n [1]. Table 3 presents the blood pressure signals and database description. A sample of the blood pressure signal is demonstrated in Fig. 3.
Table 3

Blood Pressure database and records that used in simulating algorithms dataset for record 032n.

DatabaseRecordsDescriptions for all the records
MIMIC Database Numerics032n, 033n, 037n, 039n, 041n, 048n, 052n, 054n, 055n, 208n, 209n, 210n, 211n, 212n, 213n, 214n, 215n, 216n, 218n, 219n, 220n, 221n, 224n, 225n, 226n, 230n, 231n, 232n, 233n, 235n, 237n, 240n, 241n, 242n, 243n, 245n, 248n, 252n, 253n, 254n, 255n, 259n, 260n, 262n, 264n, 267n, 268n and 269n.

60 seconds (1 min) length.

Time standard Format.

Data standard format.

MIMIC Database is called Numerics because these measurements are those that typically appear in numeric form on the ICU monitors' screens: heart rate, blood pressure (mean, systolic, diastolic), respiration rate, oxygen saturation, etc. Since the data-gathering protocol was designed to have minimal impact on patient monitoring or care, the selection of measured variables varies among these records, according to the requirements of the ICU staff for appropriate care of the patients in each case.

The measurements in these records, sampled at intervals of 1.024 seconds (0.976563 Hz),

Fig. 3

Blood pressure sample dataset for record 032n [1].

Blood Pressure database and records that used in simulating algorithms dataset for record 032n. 60 seconds (1 min) length. Time standard Format. Data standard format. MIMIC Database is called Numerics because these measurements are those that typically appear in numeric form on the ICU monitors' screens: heart rate, blood pressure (mean, systolic, diastolic), respiration rate, oxygen saturation, etc. Since the data-gathering protocol was designed to have minimal impact on patient monitoring or care, the selection of measured variables varies among these records, according to the requirements of the ICU staff for appropriate care of the patients in each case. The measurements in these records, sampled at intervals of 1.024 seconds (0.976563 Hz), Blood pressure sample dataset for record 032n [1]. These records have been used in our simulation for ECG, blood pressure and SpO2 signals to extract the vital features such as: QRS width; ST elevation; peaks number and cycle interval from ECG signal; SpO2 level from SpO2 signal; high and low blood pressure values from blood pressure signal. The databases and signal records presented in [1] have been simulated and implemented using our machine learning algorithms. This allows us to extract the essential medical features that are important for healthcare research studies. The outcome of our algorithms is presented in Table 4 as numeric values.
Table 4

Vital Features related to blood Pressure diseases extracted based on our simulating algorithms (* x means not addressed).

Simulated Dataset Descriptions
RecordSpo2 levelHeart Rates (HR)Ambulatory Blood PressureSystolic (ABP Sys)Ambulatory Blood PressureDiastolic (ABP Dias)Pulmonary Artery Pressure Systolic(PAP Sys)Pulmonary Artery Pressure Diastolic(PAP Dias)Non-invasive BP Systolic (NBP Sys)mHgNon-invasive BP Diastolic (NBP Dias)mHg
032n088xxxxxx
033n94533754xxxx
037n01228253xxxx
039n9812712073xxxx
041n958895492715xx
048n978516492xxxx
052n998516778xxxx
054n969316375xxxx
055n100101114783422xx
208n93xxxxx11696
209n91xxxxx13565
210n92xxxxx10154
221n978917763xxxx
212n0809346299xx
213n010311167xxxx
214n931049255xx15051
215n9571xx782918162
216n928211354582600
218n94709657xx00
219n947312661xx00
220n09414156xxxx
221n997618280xxxx
224n94xxxxxxx
225n96xxxxxxx
226n9713315181xxxx
230n977211254592900
231n091122575025xx
232n975710563xxxx
233nx74xxxx9757
235n959715492xx00
237n977812976382300
240n948110457391900
241n07814070273600
242n997312668321900
243n011274473520xx
245n080105497735xx
248n94114140604321xx
252n926012264xx00
253n926812968522500
254n967216193442700
255n097xx3011xx
259n8656102424420xx
260n08512235512800
262n070103604125xx
264n010200481700
267n10010013664412400
268n09912866633000
269n077117515628xx
Vital Features related to blood Pressure diseases extracted based on our simulating algorithms (* x means not addressed). Blood pressure and Spo2 datasets provide different values. According to medical guidelines, there are predefined ranges of values that represent the patient condition, which is known as " triage level". This triage level is used to evaluate the performance of the healthcare system, which is specifically focused on patient's medical assessment, e.g., monitoring the patients who have chronic heart diseases or chronic blood pressure diseases. The researchers would need to consider all the probabilities of blood pressure and Spo2 values in their simulation and implementation. Therefore, more analyses would be needed to the dataset records mentioned in [1], which is considered to be time and resources consuming. Hence, the dataset needs to be organized as presented in Table 1, Table 2, Table 3 to allow the researchers to use simplified numeric values in their research work. This essential task has been achieved in our paper so that we have done it on their behalf. Furthermore, we provide the researchers a dataset with different ranges of values that represent different triage levels. Table 5 demonstrates the dataset with all the probabilities of low blood pressure value, (mHg)high blood pressure value (mHg) and SpO2 value. Moreover, we provide new heterogeneous sources, i.e., text sources. The context of the text-inputs is provided as medical questions. These questions are expressed based on doctors' recommendations. Also, these questions are considered in the doctor diagnostics procedure. The answer to each question considers the feature of each text source. The questions are addressed manually, and all probabilities for the different answers are also considered. These questions can be summarized as follows:
Table 5

Vital features related to doctor diagnostics procedure in emergency department and disease detection.

Patient number
Simulated Dataset DescriptionsChest painShortness of BreathPalpitation.Patient at restSpO2 ValueHigh Blood Pressure (Bp) Value (mHg)Low Blood Pressure (Bp) Value (mHg)
1.NNNN971223
2.YNNN971223
3.NYNN971223
4.YYNN971223
5.NNYN971223
6.YNYN971223
7.NYYN971223
8.YYYN971223
9.NNNY971223
10.YNNY971223
11.NYNY971223
12.YYNY971223
13.NNYY971223
14.YNYY971223
15.NYYY971223
16.YYYY971223
17.NNNN921223
18.YNNN921223
19.NYNN921223
20.YYNN921223
21.NNYN921223
22.YNYN921223
23.NYYN921223
24.YYYN921223
25.NNNY921223
26.YNNY921223
27.NYNY921223
28.YYNY921223
29.NNYY921223
30.YNYY921223
31.NYYY921223
32.YYYY921223
33.NNNN971015
34.YNNN971015
35.NYNN971015
36.YYNN971015
37.NNYN971015
38.YNYN971015
39.NYYN971015
40.YYYN971015
41.NNNY971015
42.YNNY971015
43.NYNY971015
44.YYNY971015
45.NNYY971015
46.YNYY971015
47.NYYY971015
48.YYYY971015
49.NNNN921015
50.YNNN921015
51.NYNN921015
52.YYNN921015
53.NNYN921015
54.YNYN921015
55.NYYN921015
56.YYYN921015
57.NNNY921015
58.YNNY921015
59.NYNY921015
60.YYNY921015
61.NNYY921015
62.YNYY921015
63.NYYY921015
64.YYYY921015
65.NNNN97812
66.YNNN97812
67.NYNN97812
68.YYNN97812
69.NNYN97812
70.YNYN97812
71.NYYN97812
72.YYYN97812
73.NNNY97812
74.YNNY97812
75.NYNY97812
76.YYNY97812
77.NNYY97812
78.YNYY97812
79.NYYY97812
80.YYYY97812
81.NNNN92812
82.YNNN92812
83.NYNN92812
84.YYNN92812
85.NNYN92812
86.YNYN92812
87.NYYN92812
88.YYYN92812
89.NNNY92812
90.YNNY92812
91.NYNY92812
92.YYNY92812
93.NNYY92812
94.YNYY92812
95.NYYY92812
96.YYYY92812
97.NNNN80812
98.YNNN80812
99.NYNN80812
100.YYNN80812
101.NNYN80812
102.YNYN80812
103.NYYN80812
104.YYYN80812
105.NNNY80812
106.YNNY80812
107.NYNY80812
108.YYNY80812
109.NNYY80812
110.YNYY80812
111.NYYY80812
112.YYYY80812
113.NNNN801015
114.YNNN801015
115.NYNN801015
116.YYNN801015
117.NNYN801015
118.YNYN801015
119.NYYN801015
120.YYYN801015
121.NNNY801015
122.YNNY801015
123.NYNY801015
124.YYNY801015
125.NNYY801015
126.YNYY801015
127.NYYY801015
128.YYYY801015
129.NNNN801223
130.YNNN801223
131.NYNN801223
132.YYNN801223
133.NNYN801223
134.YNYN801223
135.NYYN801223
136.YYYN801223
137.NNNY801223
138.YNNY801223
139.NYNY801223
140.YYNY801223
141.NNYY801223
142.YNYY801223
143.NYYY801223
Chest pain. The answer is either (Yes) OR (No). Shortness of breath. The answer is either (Yes) OR (No). Palpitation. The answer is either (Yes) OR (No). Patient at rest. The answer is either (Yes) OR (No). Vital features related to doctor diagnostics procedure in emergency department and disease detection. According to the medical guidelines, four main ECG features, which are related to many chronic heart diseases, should be extracted. These features are presented as follows: Rhythm, which indicates the sinus bradycardia, sinus tachycardia, atrial tachycardia, atrial flutter, and sick sinus syndrome [9]. QRS complex width, which indicates the activity of the bundle branch in the heart [9]. Peak-to-peak regularity. ST elevation, which indicates acute myocardial infarction, Prinzmetal's angina, and left ventricular aneurysm [9]. In our evaluation, all the simulated ECG signals represent an abnormal ECG signal. Each signal represents a patient with a certain type of heart disease. We have extracted the four main ECG features and organized them as a new ECG dataset. The researchers can directly use this dataset in their future works. Moreover, to enrich our dataset, we have added our new ECG dataset in Table 5. This dataset becomes easy to access in case the researchers eager to use all the sources in one platform. In addition, we have added our simulation outcomes in terms of triage levels to the table. Our outcomes have already evaluated by medical doctors. Table 6 represents outcomes form our simulation of 580 patients including the formulation of 11 features dataset and variety records of ECG signals where the triage level is provided as output.
Table 6

Outcomes form our simulation for 580 patients include formulation of 11 features dataset and variety records of ECG signals and the triage level as output.

p. no.ECG RecordsSpo2H. Blood(mHg)L. Blood(mHg)Chest PainShort-ness of BreathPalpitation.rest?PeaksQRS widthPeak to PeakST El.OutputTriage level
1Sleep apnea Records972312falsefalsefalsefalse670.06RegulartrueSick
2972312falsefalsefalsetrue670.06RegulartrueSick
3972312falsefalsetruefalse670.06RegulartrueSick
4972312falsefalsetruetrue670.06RegulartrueSick
5972312falsetruefalsefalse670.06RegulartrueSick
6972312falsetruefalsetrue670.06RegulartrueSick
7972312falsetruetruefalse670.06RegulartrueSick
8972312falsetruetruetrue670.06RegulartrueUrgent
9972312truefalsefalsefalse670.06RegulartrueSick
10972312truefalsefalsetrue670.06RegulartrueUrgent
11972312truefalsetruefalse670.06RegulartrueUrgent
12972312truefalsetruetrue670.06RegulartrueUrgent
13972312truetruefalsefalse670.06RegulartrueUrgent
14972312truetruefalsetrue670.06RegulartrueUrgent
15972312truetruetruefalse670.06Regulartruerisk
16972312truetruetruetrue670.06Regulartruerisk
17922312falsefalsefalsefalse670.06RegulartrueSick
18922312falsefalsefalsetrue670.06RegulartrueSick
19922312falsefalsetruefalse670.06RegulartrueSick
20922312falsefalsetruetrue670.06RegulartrueUrgent
21922312falsetruefalsefalse670.06RegulartrueSick
22922312falsetruefalsetrue670.06RegulartrueSick
23922312falsetruetruefalse670.06RegulartrueUrgent
24922312falsetruetruetrue670.06RegulartrueUrgent
25922312truefalsefalsefalse670.06RegulartrueUrgent
26922312truefalsefalsetrue670.06RegulartrueUrgent
27922312truefalsetruefalse670.06Regulartruerisk
28922312truefalsetruetrue670.06Regulartruerisk
29922312truetruefalsefalse670.06RegulartrueUrgent
30922312truetruefalsetrue670.06RegulartrueUrgent
31922312truetruetruefalse670.06Regulartruerisk
32922312truetruetruetrue670.06Regulartruerisk
33971510falsefalsefalsefalse670.06RegulartrueSick
34971510falsefalsefalsetrue670.06RegulartrueSick
35971510falsefalsetruefalse670.06RegulartrueSick
36971510falsefalsetruetrue670.06RegulartrueSick
37971510falsetruefalsefalse670.06RegulartrueSick
38971510falsetruefalsetrue670.06RegulartrueSick
39971510falsetruetruefalse670.06RegulartrueSick
40971510falsetruetruetrue670.06RegulartrueSick
41971510truefalsefalsefalse670.06RegulartrueSick
42971510truefalsefalsetrue670.06RegulartrueSick
43971510truefalsetruefalse670.06RegulartrueUrgent
44971510truefalsetruetrue670.06RegulartrueUrgent
45971510truetruefalsefalse670.06RegulartrueSick
46971510truetruefalsetrue670.06RegulartrueUrgent
47971510truetruetruefalse670.06RegulartrueUrgent
48971510truetruetruetrue670.06RegulartrueUrgent
49921510falsefalsefalsefalse670.06RegulartrueSick
50921510falsefalsefalsetrue670.06RegulartrueSick
51921510falsefalsetruefalse670.06RegulartrueSick
52921510falsefalsetruetrue670.06RegulartrueSick
53921510falsetruefalsefalse670.06RegulartrueSick
54921510falsetruefalsetrue670.06RegulartrueSick
55921510falsetruetruefalse670.06RegulartrueSick
56921510falsetruetruetrue670.06RegulartrueUrgent
57921510truefalsefalsefalse670.06RegulartrueSick
58921510truefalsefalsetrue670.06RegulartrueUrgent
59921510truefalsetruefalse670.06RegulartrueUrgent
60921510truefalsetruetrue670.06RegulartrueUrgent
61921510truetruefalsefalse670.06RegulartrueUrgent
62921510truetruefalsetrue670.06RegulartrueUrgent
63921510truetruetruefalse670.06Regulartruerisk
64921510truetruetruetrue670.06Regulartruerisk
6597128falsefalsefalsefalse670.06RegulartrueCold State
6697128falsefalsefalsetrue670.06RegulartrueCold State
6797128falsefalsetruefalse670.06RegulartrueSick
6897128falsefalsetruetrue670.06RegulartrueSick
6997128falsetruefalsefalse670.06RegulartrueSick
7097128falsetruefalsetrue670.06RegulartrueSick
7197128falsetruetruefalse670.06RegulartrueSick
7297128falsetruetruetrue670.06RegulartrueSick
7397128truefalsefalsefalse670.06RegulartrueSick
7497128truefalsefalsetrue670.06RegulartrueSick
7597128truefalsetruefalse670.06RegulartrueSick
7697128truefalsetruetrue670.06RegulartrueUrgent
7797128truetruefalsefalse670.06RegulartrueSick
7897128truetruefalsetrue670.06RegulartrueSick
7997128truetruetruefalse670.06RegulartrueUrgent
8097128truetruetruetrue670.06RegulartrueUrgent
8192128falsefalsefalsefalse670.06RegulartrueSick
8292128falsefalsefalsetrue670.06RegulartrueSick
8392128falsefalsetruefalse670.06RegulartrueSick
8492128falsefalsetruetrue670.06RegulartrueSick
8592128falsetruefalsefalse670.06RegulartrueSick
8692128falsetruefalsetrue670.06RegulartrueSick
8792128falsetruetruefalse670.06RegulartrueSick
8892128falsetruetruetrue670.06RegulartrueSick
8992128truefalsefalsefalse670.06RegulartrueSick
9092128truefalsefalsetrue670.06RegulartrueSick
9192128truefalsetruefalse670.06RegulartrueUrgent
9292128truefalsetruetrue670.06RegulartrueUrgent
9392128truetruefalsefalse670.06RegulartrueSick
9492128truetruefalsetrue670.06RegulartrueUrgent
9592128truetruetruefalse670.06RegulartrueUrgent
9692128truetruetruetrue670.06RegulartrueUrgent
9780128falsefalsefalsefalse670.06RegulartrueSick
9880128falsefalsefalsetrue670.06RegulartrueSick
9980128falsefalsetruefalse670.06RegulartrueSick
10080128falsefalsetruetrue670.06RegulartrueSick
10180128falsetruefalsefalse670.06RegulartrueSick
10280128falsetruefalsetrue670.06RegulartrueSick
10380128falsetruetruefalse670.06RegulartrueSick
10480128falsetruetruetrue670.06RegulartrueUrgent
10580128truefalsefalsefalse670.06RegulartrueSick
10680128truefalsefalsetrue670.06RegulartrueUrgent
10780128truefalsetruefalse670.06RegulartrueUrgent
10880128truefalsetruetrue670.06RegulartrueUrgent
10980128truetruefalsefalse670.06RegulartrueUrgent
11080128truetruefalsetrue670.06RegulartrueUrgent
11180128truetruetruefalse670.06Regulartruerisk
11280128truetruetruetrue670.06Regulartruerisk
113801510falsefalsefalsefalse670.06RegulartrueSick
114801510falsefalsefalsetrue670.06RegulartrueSick
115801510falsefalsetruefalse670.06RegulartrueSick
116801510falsefalsetruetrue670.06RegulartrueUrgent
117801510falsetruefalsefalse670.06RegulartrueSick
118801510falsetruefalsetrue670.06RegulartrueSick
119801510falsetruetruefalse670.06RegulartrueUrgent
120801510falsetruetruetrue670.06RegulartrueUrgent
121Long Term ST Records801510truefalsefalsefalse670.06RegulartrueUrgent
122801510truefalsefalsetrue670.06RegulartrueUrgent
123801510truefalsetruefalse670.06Regulartruerisk
124801510truefalsetruetrue670.06Regulartruerisk
125801510truetruefalsefalse670.06RegulartrueUrgent
126801510truetruefalsetrue670.06RegulartrueUrgent
127801510truetruetruefalse670.06Regulartruerisk
128801510truetruetruetrue670.06Regulartruerisk
129802312falsefalsefalsefalse670.06RegulartrueSick
130802312falsefalsefalsetrue670.06RegulartrueSick
131802312falsefalsetruefalse670.06RegulartrueUrgent
132802312falsefalsetruetrue670.06RegulartrueUrgent
133802312falsetruefalsefalse670.06RegulartrueSick
134802312falsetruefalsetrue670.06RegulartrueUrgent
135802312falsetruetruefalse670.06RegulartrueUrgent
136802312falsetruetruetrue670.06RegulartrueUrgent
137802312truefalsefalsefalse670.06RegulartrueUrgent
138802312truefalsefalsetrue670.06RegulartrueUrgent
139802312truefalsetruefalse670.06Regulartruerisk
140802312truefalsetruetrue670.06Regulartruerisk
141802312truetruefalsefalse670.06Regulartruerisk
142802312truetruefalsetrue670.06Regulartruerisk
143802312truetruetruefalse670.06Regulartruerisk
144802312truetruetruetrue670.06Regulartruerisk
145Arrythmia Records972312falsefalsefalsefalse540.5RegularfalseSick
146972312falsefalsefalsetrue540.5RegularfalseSick
147972312falsefalsetruefalse540.5RegularfalseSick
148972312falsefalsetruetrue540.5RegularfalseSick
149972312falsetruefalsefalse540.5RegularfalseSick
150972312falsetruefalsetrue540.5RegularfalseSick
151972312falsetruetruefalse540.5RegularfalseSick
152972312falsetruetruetrue540.5RegularfalseSick
153972312truefalsefalsefalse540.5RegularfalseSick
154972312truefalsefalsetrue540.5RegularfalseSick
155972312truefalsetruefalse540.5RegularfalseUrgent
156972312truefalsetruetrue540.5RegularfalseUrgent
157972312truetruefalsefalse540.5RegularfalseSick
158972312truetruefalsetrue540.5RegularfalseUrgent
159972312truetruetruefalse540.5RegularfalseUrgent
160972312truetruetruetrue540.5RegularfalseUrgent
161922312falsefalsefalsefalse540.5RegularfalseSick
162922312falsefalsefalsetrue540.5RegularfalseSick
163922312falsefalsetruefalse540.5RegularfalseSick
164922312falsefalsetruetrue540.5RegularfalseSick
165922312falsetruefalsefalse540.5RegularfalseSick
166922312falsetruefalsetrue540.5RegularfalseSick
167922312falsetruetruefalse540.5RegularfalseSick
168922312falsetruetruetrue540.5RegularfalseUrgent
169922312truefalsefalsefalse540.5RegularfalseSick
170922312truefalsefalsetrue540.5RegularfalseUrgent
171922312truefalsetruefalse540.5RegularfalseUrgent
172922312truefalsetruetrue540.5RegularfalseUrgent
173922312truetruefalsefalse540.5RegularfalseUrgent
174922312truetruefalsetrue540.5RegularfalseUrgent
175922312truetruetruefalse540.5Regularfalserisk
176922312truetruetruetrue540.5Regularfalserisk
177971510falsefalsefalsefalse540.5RegularfalseCold State
178971510falsefalsefalsetrue540.5RegularfalseCold State
179971510falsefalsetruefalse540.5RegularfalseSick
180971510falsefalsetruetrue540.5RegularfalseSick
181971510falsetruefalsefalse540.5RegularfalseSick
182971510falsetruefalsetrue540.5RegularfalseSick
183971510falsetruetruefalse540.5RegularfalseSick
184971510falsetruetruetrue540.5RegularfalseSick
185971510truefalsefalsefalse540.5RegularfalseSick
186971510truefalsefalsetrue540.5RegularfalseSick
187971510truefalsetruefalse540.5RegularfalseSick
188971510truefalsetruetrue540.5RegularfalseUrgent
189971510truetruefalsefalse540.5RegularfalseSick
190971510truetruefalsetrue540.5RegularfalseSick
191971510truetruetruefalse540.5RegularfalseUrgent
192971510truetruetruetrue540.5RegularfalseUrgent
193921510falsefalsefalsefalse540.5RegularfalseSick
194921510falsefalsefalsetrue540.5RegularfalseSick
195921510falsefalsetruefalse540.5RegularfalseSick
196921510falsefalsetruetrue540.5RegularfalseSick
197921510falsetruefalsefalse540.5RegularfalseSick
198921510falsetruefalsetrue540.5RegularfalseSick
199921510falsetruetruefalse540.5RegularfalseSick
200921510falsetruetruetrue540.5RegularfalseSick
201921510truefalsefalsefalse540.5RegularfalseSick
202921510truefalsefalsetrue540.5RegularfalseSick
203921510truefalsetruefalse540.5RegularfalseUrgent
204921510truefalsetruetrue540.5RegularfalseUrgent
205921510truetruefalsefalse540.5RegularfalseSick
206921510truetruefalsetrue540.5RegularfalseUrgent
207921510truetruetruefalse540.5RegularfalseUrgent
208921510truetruetruetrue540.5RegularfalseUrgent
20997128falsefalsefalsefalse540.5RegularfalseCold State
21097128falsefalsefalsetrue540.5RegularfalseCold State
21197128falsefalsetruefalse540.5RegularfalseSick
21297128falsefalsetruetrue540.5RegularfalseSick
21397128falsetruefalsefalse540.5RegularfalseCold State
21497128falsetruefalsetrue540.5RegularfalseCold State
21597128falsetruetruefalse540.5RegularfalseSick
21697128falsetruetruetrue540.5RegularfalseSick
21797128truefalsefalsefalse540.5RegularfalseSick
21897128truefalsefalsetrue540.5RegularfalseSick
21997128truefalsetruefalse540.5RegularfalseSick
22097128truefalsetruetrue540.5RegularfalseSick
22197128truetruefalsefalse540.5RegularfalseSick
22297128truetruefalsetrue540.5RegularfalseSick
22397128truetruetruefalse540.5RegularfalseSick
22497128truetruetruetrue540.5RegularfalseUrgent
22592128falsefalsefalsefalse540.5RegularfalseCold State
22692128falsefalsefalsetrue540.5RegularfalseCold State
22792128falsefalsetruefalse540.5RegularfalseSick
22892128falsefalsetruetrue540.5RegularfalseSick
22992128falsetruefalsefalse540.5RegularfalseSick
23092128falsetruefalsetrue540.5RegularfalseSick
23192128falsetruetruefalse540.5RegularfalseSick
23292128falsetruetruetrue540.5RegularfalseSick
23392128truefalsefalsefalse540.5RegularfalseSick
23492128truefalsefalsetrue540.5RegularfalseSick
23592128truefalsetruefalse540.5RegularfalseSick
23692128truefalsetruetrue540.5RegularfalseUrgent
23792128truetruefalsefalse540.5RegularfalseSick
23892128truetruefalsetrue540.5RegularfalseSick
23992128truetruetruefalse540.5RegularfalseUrgent
24092128truetruetruetrue540.5RegularfalseUrgent
24180128falsefalsefalsefalse540.5RegularfalseSick
24280128falsefalsefalsetrue540.5RegularfalseSick
24380128falsefalsetruefalse540.5RegularfalseSick
24480128falsefalsetruetrue540.5RegularfalseSick
24580128falsetruefalsefalse540.5RegularfalseSick
24680128falsetruefalsetrue540.5RegularfalseSick
24780128falsetruetruefalse540.5RegularfalseSick
24880128falsetruetruetrue540.5RegularfalseSick
24980128truefalsefalsefalse540.5RegularfalseSick
25080128truefalsefalsetrue540.5RegularfalseSick
25180128truefalsetruefalse540.5RegularfalseUrgent
25280128truefalsetruetrue540.5RegularfalseUrgent
25380128truetruefalsefalse540.5RegularfalseSick
25480128truetruefalsetrue540.5RegularfalseUrgent
25580128truetruetruefalse540.5RegularfalseUrgent
25680128truetruetruetrue540.5RegularfalseUrgent
257801510falsefalsefalsefalse540.5RegularfalseSick
258801510falsefalsefalsetrue540.5RegularfalseSick
259801510falsefalsetruefalse540.5RegularfalseSick
260801510falsefalsetruetrue540.5RegularfalseSick
261801510falsetruefalsefalse540.5RegularfalseSick
262801510falsetruefalsetrue540.5RegularfalseSick
263801510falsetruetruefalse540.5RegularfalseSick
264801510falsetruetruetrue540.5RegularfalseUrgent
265801510truefalsefalsefalse540.5RegularfalseSick
266801510truefalsefalsetrue540.5RegularfalseUrgent
267801510truefalsetruefalse540.5RegularfalseUrgent
268801510truefalsetruetrue540.5RegularfalseUrgent
269801510truetruefalsefalse540.5RegularfalseUrgent
270801510truetruefalsetrue540.5RegularfalseUrgent
271801510truetruetruefalse540.5Regularfalserisk
272801510truetruetruetrue540.5Regularfalserisk
273802312falsefalsefalsefalse540.5RegularfalseSick
274802312falsefalsefalsetrue540.5RegularfalseSick
275802312falsefalsetruefalse540.5RegularfalseSick
276802312falsefalsetruetrue540.5RegularfalseUrgent
277802312falsetruefalsefalse540.5RegularfalseSick
278802312falsetruefalsetrue540.5RegularfalseSick
279802312falsetruetruefalse540.5RegularfalseUrgent
280802312falsetruetruetrue540.5RegularfalseUrgent
281802312truefalsefalsefalse540.5RegularfalseUrgent
282802312truefalsefalsetrue540.5RegularfalseUrgent
283802312truefalsetruefalse540.5Regularfalserisk
284802312truefalsetruetrue540.5Regularfalserisk
285802312truetruefalsefalse540.5RegularfalseUrgent
286802312truetruefalsetrue540.5RegularfalseUrgent
287802312truetruetruefalse540.5Regularfalserisk
288802312truetruetruetrue540.5Regularfalserisk
289972312falsefalsefalsefalse770.047RegulartrueSick
290972312falsefalsefalsetrue770.047RegulartrueSick
291972312falsefalsetruefalse770.047RegulartrueSick
292972312falsefalsetruetrue770.047RegulartrueSick
293972312falsetruefalsefalse770.047RegulartrueSick
294972312falsetruefalsetrue770.047RegulartrueSick
295972312falsetruetruefalse770.047RegulartrueUrgent
296972312falsetruetruetrue770.047RegulartrueUrgent
297972312truefalsefalsefalse770.047RegulartrueUrgent
298972312truefalsefalsetrue770.047RegulartrueUrgent
299972312truefalsetruefalse770.047Regulartruerisk
300972312truefalsetruetrue770.047Regulartruerisk
301972312truetruefalsefalse770.047RegulartrueUrgent
302972312truetruefalsetrue770.047RegulartrueUrgent
303972312truetruetruefalse770.047Regulartruerisk
304972312truetruetruetrue770.047Regulartruerisk
305922312falsefalsefalsefalse770.047RegulartrueSick
306922312falsefalsefalsetrue770.047RegulartrueSick
307922312falsefalsetruefalse770.047RegulartrueUrgent
308922312falsefalsetruetrue770.047RegulartrueUrgent
309922312falsetruefalsefalse770.047RegulartrueSick
310922312falsetruefalsetrue770.047RegulartrueSick
311922312falsetruetruefalse770.047RegulartrueUrgent
312922312falsetruetruetrue770.047RegulartrueUrgent
313922312truefalsefalsefalse770.047RegulartrueUrgent
314922312truefalsefalsetrue770.047RegulartrueUrgent
315922312truefalsetruefalse770.047Regulartruerisk
316922312truefalsetruetrue770.047Regulartruerisk
317922312truetruefalsefalse770.047Regulartruerisk
318922312truetruefalsetrue770.047Regulartruerisk
319922312truetruetruefalse770.047Regulartruerisk
320922312truetruetruetrue770.047Regulartruerisk
321971510falsefalsefalsefalse770.047RegulartrueSick
322971510falsefalsefalsetrue770.047RegulartrueSick
323971510falsefalsetruefalse770.047RegulartrueSick
324971510falsefalsetruetrue770.047RegulartrueSick
325971510falsetruefalsefalse770.047RegulartrueSick
326971510falsetruefalsetrue770.047RegulartrueSick
327971510falsetruetruefalse770.047RegulartrueSick
328971510falsetruetruetrue770.047RegulartrueSick
329971510truefalsefalsefalse770.047RegulartrueSick
330971510truefalsefalsetrue770.047RegulartrueSick
331971510truefalsetruefalse770.047RegulartrueUrgent
332971510truefalsetruetrue770.047RegulartrueUrgent
333971510truetruefalsefalse770.047RegulartrueUrgent
334971510truetruefalsetrue770.047RegulartrueUrgent
335971510truetruetruefalse770.047Regulartruerisk
336971510truetruetruetrue770.047Regulartruerisk
337921510falsefalsefalsefalse770.047RegulartrueSick
338921510falsefalsefalsetrue770.047RegulartrueSick
339921510falsefalsetruefalse770.047RegulartrueSick
340921510falsefalsetruetrue770.047RegulartrueSick
341921510falsetruefalsefalse770.047RegulartrueSick
342921510falsetruefalsetrue770.047RegulartrueSick
343921510falsetruetruefalse770.047RegulartrueUrgent
344921510falsetruetruetrue770.047RegulartrueUrgent
345921510truefalsefalsefalse770.047RegulartrueUrgent
346921510truefalsefalsetrue770.047RegulartrueUrgent
347921510truefalsetruefalse770.047Regulartruerisk
348921510truefalsetruetrue770.047Regulartruerisk
349921510truetruefalsefalse770.047RegulartrueUrgent
350921510truetruefalsetrue770.047RegulartrueUrgent
351921510truetruetruefalse770.047Regulartruerisk
352921510truetruetruetrue770.047Regulartruerisk
35397128falsefalsefalsefalse770.047RegulartrueCold State
35497128falsefalsefalsetrue770.047RegulartrueSick
35597128falsefalsetruefalse770.047RegulartrueSick
35697128falsefalsetruetrue770.047RegulartrueSick
35797128falsetruefalsefalse770.047RegulartrueSick
35897128falsetruefalsetrue770.047RegulartrueSick
35997128falsetruetruefalse770.047RegulartrueSick
36097128falsetruetruetrue770.047RegulartrueSick
36197128truefalsefalsefalse770.047RegulartrueSick
36297128truefalsefalsetrue770.047RegulartrueSick
36397128truefalsetruefalse770.047RegulartrueUrgent
36497128truefalsetruetrue770.047RegulartrueUrgent
36597128truetruefalsefalse770.047RegulartrueSick
36697128truetruefalsetrue770.047RegulartrueSick
36797128truetruetruefalse770.047RegulartrueUrgent
36897128truetruetruetrue770.047RegulartrueUrgent
36992128falsefalsefalsefalse770.047RegulartrueSick
37092128falsefalsefalsetrue770.047RegulartrueSick
37192128falsefalsetruefalse770.047RegulartrueSick
37292128falsefalsetruetrue770.047RegulartrueSick
37392128falsetruefalsefalse770.047RegulartrueSick
37492128falsetruefalsetrue770.047RegulartrueSick
37592128falsetruetruefalse770.047RegulartrueSick
37692128falsetruetruetrue770.047RegulartrueSick
37792128truefalsefalsefalse770.047RegulartrueSick
37892128truefalsefalsetrue770.047RegulartrueSick
37992128truefalsetruefalse770.047RegulartrueUrgent
38092128truefalsetruetrue770.047RegulartrueUrgent
38192128truetruefalsefalse770.047RegulartrueUrgent
38292128truetruefalsetrue770.047RegulartrueUrgent
38392128truetruetruefalse770.047Regulartruerisk
38492128truetruetruetrue770.047Regulartruerisk
38580128falsefalsefalsefalse770.047RegulartrueSick
38680128falsefalsefalsetrue770.047RegulartrueSick
38780128falsefalsetruefalse770.047RegulartrueSick
38880128falsefalsetruetrue770.047RegulartrueSick
38980128falsetruefalsefalse770.047RegulartrueSick
39080128falsetruefalsetrue770.047RegulartrueSick
39180128falsetruetruefalse770.047RegulartrueUrgent
39280128falsetruetruetrue770.047RegulartrueUrgent
39380128truefalsefalsefalse770.047RegulartrueUrgent
39480128truefalsefalsetrue770.047RegulartrueUrgent
39580128truefalsetruefalse770.047Regulartruerisk
39680128truefalsetruetrue770.047Regulartruerisk
39780128truetruefalsefalse770.047RegulartrueUrgent
39880128truetruefalsetrue770.047RegulartrueUrgent
39980128truetruetruefalse770.047Regulartruerisk
40080128truetruetruetrue770.047Regulartruerisk
401801510falsefalsefalsefalse770.047RegulartrueSick
402801510falsefalsefalsetrue770.047RegulartrueSick
403801510falsefalsetruefalse770.047RegulartrueUrgent
404801510falsefalsetruetrue770.047RegulartrueUrgent
405801510falsetruefalsefalse770.047RegulartrueSick
406801510falsetruefalsetrue770.047RegulartrueSick
407801510falsetruetruefalse770.047RegulartrueUrgent
408801510falsetruetruetrue770.047RegulartrueUrgent
409801510truefalsefalsefalse770.047RegulartrueUrgent
410801510truefalsefalsetrue770.047RegulartrueUrgent
411801510truefalsetruefalse770.047Regulartruerisk
412801510truefalsetruetrue770.047Regulartruerisk
413801510truetruefalsefalse770.047Regulartruerisk
414801510truetruefalsetrue770.047Regulartruerisk
415801510truetruetruefalse770.047Regulartruerisk
416801510truetruetruetrue770.047Regulartruerisk
417802312falsefalsefalsefalse770.047RegulartrueSick
418802312falsefalsefalsetrue770.047RegulartrueSick
419802312falsefalsetruefalse770.047RegulartrueUrgent
420802312falsefalsetruetrue770.047RegulartrueUrgent
421802312falsetruefalsefalse770.047RegulartrueUrgent
422802312falsetruefalsetrue770.047RegulartrueUrgent
423802312falsetruetruefalse770.047Regulartruerisk
424802312falsetruetruetrue770.047Regulartruerisk
425802312truefalsefalsefalse770.047Regulartruerisk
426802312truefalsefalsetrue770.047Regulartruerisk
427802312truefalsetruefalse770.047Regulartruerisk
428802312truefalsetruetrue770.047Regulartruerisk
429802312truetruefalsefalse770.047Regulartruerisk
430802312truetruefalsetrue770.047Regulartruerisk
431802312truetruetruefalse770.047Regulartruerisk
432802312truetruetruetrue770.047Regulartruerisk
433Heart failure records972312falsefalsefalsefalse640.169RegularfalseCold State
434972312falsefalsefalsetrue640.169RegularfalseCold State
435972312falsefalsetruefalse640.169RegularfalseSick
436972312falsefalsetruetrue640.169RegularfalseSick
437972312falsetruefalsefalse640.169RegularfalseSick
438972312falsetruefalsetrue640.169RegularfalseSick
439972312falsetruetruefalse640.169RegularfalseSick
440972312falsetruetruetrue640.169RegularfalseSick
441972312truefalsefalsefalse640.169RegularfalseSick
442972312truefalsefalsetrue640.169RegularfalseSick
443972312truefalsetruefalse640.169RegularfalseSick
444972312truefalsetruetrue640.169RegularfalseUrgent
445972312truetruefalsefalse640.169RegularfalseSick
446972312truetruefalsetrue640.169RegularfalseSick
447972312truetruetruefalse640.169RegularfalseUrgent
448972312truetruetruetrue640.169RegularfalseUrgent
449922312falsefalsefalsefalse640.169RegularfalseSick
450922312falsefalsefalsetrue640.169RegularfalseSick
451922312falsefalsetruefalse640.169RegularfalseSick
452922312falsefalsetruetrue640.169RegularfalseSick
453922312falsetruefalsefalse640.169RegularfalseSick
454922312falsetruefalsetrue640.169RegularfalseSick
455922312falsetruetruefalse640.169RegularfalseSick
456922312falsetruetruetrue640.169RegularfalseSick
457922312truefalsefalsefalse640.169RegularfalseSick
458922312truefalsefalsetrue640.169RegularfalseSick
459922312truefalsetruefalse640.169RegularfalseUrgent
460922312truefalsetruetrue640.169RegularfalseUrgent
461922312truetruefalsefalse640.169RegularfalseSick
462922312truetruefalsetrue640.169RegularfalseUrgent
463922312truetruetruefalse640.169RegularfalseUrgent
464922312truetruetruetrue640.169RegularfalseUrgent
465971510falsefalsefalsefalse640.169RegularfalseCold State
466971510falsefalsefalsetrue640.169RegularfalseCold State
467971510falsefalsetruefalse640.169RegularfalseSick
468971510falsefalsetruetrue640.169RegularfalseSick
469971510falsetruefalsefalse640.169RegularfalseCold State
470971510falsetruefalsetrue640.169RegularfalseCold State
471971510falsetruetruefalse640.169RegularfalseSick
472971510falsetruetruetrue640.169RegularfalseSick
473971510truefalsefalsefalse640.169RegularfalseSick
474971510truefalsefalsetrue640.169RegularfalseSick
475971510truefalsetruefalse640.169RegularfalseSick
476971510truefalsetruetrue640.169RegularfalseSick
477971510truetruefalsefalse640.169RegularfalseSick
478971510truetruefalsetrue640.169RegularfalseSick
479971510truetruetruefalse640.169RegularfalseSick
480971510truetruetruetrue640.169RegularfalseUrgent
481921510falsefalsefalsefalse640.169RegularfalseCold State
482921510falsefalsefalsetrue640.169RegularfalseCold State
483921510falsefalsetruefalse640.169RegularfalseSick
484921510falsefalsetruetrue640.169RegularfalseSick
485921510falsetruefalsefalse640.169RegularfalseSick
486921510falsetruefalsetrue640.169RegularfalseSick
487921510falsetruetruefalse640.169RegularfalseSick
488921510falsetruetruetrue640.169RegularfalseSick
489921510truefalsefalsefalse640.169RegularfalseSick
490921510truefalsefalsetrue640.169RegularfalseSick
491921510truefalsetruefalse640.169RegularfalseSick
492921510truefalsetruetrue640.169RegularfalseUrgent
493921510truetruefalsefalse640.169RegularfalseSick
494921510truetruefalsetrue640.169RegularfalseSick
495921510truetruetruefalse640.169RegularfalseUrgent
496921510truetruetruetrue640.169RegularfalseUrgent
49797128falsefalsefalsefalse640.169RegularfalseCold State
49897128falsefalsefalsetrue640.169RegularfalseCold State
49997128falsefalsetruefalse640.169RegularfalseCold State
50097128falsefalsetruetrue640.169RegularfalseCold State
501Normal ECG972312YYNNnormalRegularSick
502972312YYNYnormalRegularSick
503972312YYYNnormalRegularSick
504972312YYYYnormalRegularSick
505971510YYNNnormalRegularSick
506971510YYNYnormalRegularSick
507971510YYYNnormalRegularSick
508971510YYYYnormalRegularSick
50997128YYNNnormalRegularCold State
51097128YYNYnormalRegularSick
51197128YYYNnormalRegularSick
51297128YYYYnormalRegularSick
51397128NNYYnormalRegularCold State
51497128NNYNnormalRegularCold State
51597128NNNYnormalRegularNormal
51697128NNNNnormalRegularNormal
51797128YNYYnormalRegularSick
51897128YNYNnormalRegularSick
51997128YNNYnormalRegularCold State
52097128YNNNnormalRegularCold State
52197128NYYYnormalRegularCold State
52297128NYNYnormalRegularCold State
52397128NYYNnormalRegularCold State
52497128NYNNnormalRegularCold State
52597158YNYYnormalRegularSick
52697158YNYNnormalRegularSick
52797158YNNYnormalRegularCold State
52897158YNNNnormalRegularCold State
52997158NYYYnormalRegularCold State
53097158NYNYnormalRegularCold State
53197158NYYNnormalRegularCold State
53297158NYNNnormalRegularCold State
533971210YNYYnormalRegularSick
534971210YNYNnormalRegularSick
535971210YNNYnormalRegularCold State
536971210YNNNnormalRegularCold State
537971210YYYYnormalRegularSick
538971210YYYNnormalRegularSick
539971210YYNYnormalRegularCold State
540971210YYNNnormalRegularCold State
541971210NYYYnormalRegularCold State
542971210NYNYnormalRegularCold State
543971210NYYNnormalRegularCold State
544971210NYNNnormalRegularCold State
54597128YNYYnormalRegularSick
54697128YNYNnormalRegularSick
54797128YNNYnormalRegularCold State
54897128YNNNnormalRegularCold State
54997128YYYYnormalRegularSick
55097128YYYNnormalRegularSick
55197128YYNYnormalRegularCold State
55297128YYNNnormalRegularCold State
553Normal ECG with HR 11097128NNNY110RegularNormal
55497128NNNY110RegularNormal
55597128NNNY110RegularNormal
55697128NNNY110RegularNormal
55797128NNNY110RegularNormal
55897128NNNY110RegularNormal
55997128NNNY110RegularNormal
56097128NNNY110RegularNormal
56197128NNNY110RegularNormal
56297128NNNY110RegularNormal
56397128NNNY110RegularNormal
56497128NNNY110RegularNormal
56597128NNNY110RegularNormal
56697128NNNY110RegularNormal
56797128NNNY110RegularNormal
56897128NNNY110RegularNormal
56997128NNNY110RegularNormal
57097128NNNY110RegularNormal
57197128NNNY110RegularNormal
57297128NNNY110RegularNormal
57392128NNNY110RegularSick
57492128NNNY110RegularSick
57592128NNNY110RegularSick
57692128NNNY110RegularSick
57792128NNNY110RegularSick
57892128NNNY110RegularSick
57992128NNNY110RegularSick
58092128NNNY110RegularSick
Outcomes form our simulation for 580 patients include formulation of 11 features dataset and variety records of ECG signals and the triage level as output. Table 7 presents dataset used in our paper [7] to provide different packages of healthcare services in the telemedicine environment.
Table 7

formulation of 10 patients’ dataset for evaluating healthcare services provisioning system in telemedicine environment.

Patient Alias NameECGrecord namespo2 recordBlood Pressure record (High)Blood Pressure record (Low)Patient index in MSHA simulationSpo2 valueHigh Blood Pressure value (mHg)Low Blood Pressure value (mHg)LocationChest PainShortness of Breath.Palpitationsrest?ECG Number of PeaksQRS widthP-P IntervalST Elevation
Mrs. smitha01259n032n209n144802312Hometruetruetruetrue670.060.065763true
Ross105052n048n414n36397128Hometruefalsetruefalse770.0470.266642true
Monica1030481n032n209n160972312Hometruetruetruetrue540.50.037372false
Joey105210n048n33n342921510Homefalsetruefalsetrue770.0470.266642true
Sally107217n032n209n436972312Homefalsefalsetruetrue640.1690.336318false
Jamesnormal048n219n219n6597128Homefalsefalsefalsefalse670.060.065763true
Rayannormal052n048n414n6697128Homefalsefalsefalsetrue670.060.065763true
Chandernormal054n219n219n51597128HomeNNNYnormal
Sarahnormal048n048n414n51697128HomeNNNNnormal
Phebynormal052n219n219n55397128HomeNNNY110normal
formulation of 10 patients’ dataset for evaluating healthcare services provisioning system in telemedicine environment.

Experimental design, materials and methods

Simulation setup

The software architecture of our algorithms is implemented using JAVA programming language. This is because JAVA has many benefits, such as: (a) real-time implementation, (b) parallel execution, (c) usage from anywhere by all interested parties, (d) ability to run JAVA-based applications on different platforms, (e) and compatibility to be used with different operating systems, e.g., Android, Windows, and Linux. The advantages of using JAVA have paved the way for the implementation of our algorithms in different hardware platforms. XAMMP has also been used. Specifically, XAMPP is a small and light Apache distribution tool that contains the most common web development technologies in a single package. XAMPP is a free/open-source software, and its name stands for (X) cross-platform for Web server, HTTP Apache Server, (M) MySQL database, (P) PHP scripts writing language, and (P) Perl programming language. In our paper, the dataset is re-organized and re-formatted in structure dataset format. The dataset is represented in terms of tables to illustrate the patient's profile and the type of diseases.

Computational analytic methods and codes

To extract the dataset mentioned in Table 4, Table 5, Table 6, advanced processing algorithms have been applied to the signals mentioned in Table 1, Table 2, Table 3. To this end, a multi-function data processing algorithm is proposed and implemented [7] in order to extract the essential features from each source individually. Each signal is represented by an array. According to the extracted dataset, each element in the signal has two values. The first value represents time and the second represents voltage. The array of each signal has two columns (each column represents a value). The number of rows is defined by the number of elements in the signal, which starts from (0) and ends at (n). The array of text feature is 1 × 4 because there are four variables that represent four features. A real-time data processing algorithm have been utilized to extract ECG features. The ECG signal is represented by an array of two columns (time in (ms) and voltage in (mv)). These values have been used to extract the features. The ECG signal provides many cycles. One ECG cycle has many ECG features such as: Rythem; QRS; ST; and P-P. For each cycle, the signal values in time are varied around the zero lines. These values are used to split the ECG cycle to Up and Down halves, then sorting the upper half based on voltage values. This is then applied to find the maximum point, which is represented by the R point. Accordingly, the upper half of the ECG cycle can be splatted into right and half. As such, by using certain functions to sort the values of the ECG cycle for each half (Up_Lift and Up_right) based on (t) value and (v) value, the location of Q and S points can be found. Moreover, the ST elevation can be determined based on the differences of (t) and (v) values using the subtraction functions. The SpO2 and blood pressure values have been calculated as mentioned in [6]. The proposed algorithm is presented as pseudo-codes to enable the researchers to implement it in any software platform. Moreover, the algorithm is implemented using Java code, which is provided in the attached appendix.

Ethics statement

The authors would like to point out that the primary data sources are available in a public repository and given in PhysioNet online library [1]. PhysioNet online library includes many types of medical raw datasets. PhysioNet online library gives the permission to all researchers around the world to download and use the raw datasets. However, our main contribution is presented in applying signal processing algorithms in order to extract the essential vital features from the raw datasets. Consequently, the essential raw dataset and the outcomes of the simulated data are organized, structured, formulated and presented as multi diseases dataset. Finally, the authors would like to indicate that neither human subjects nor animal experiments are involved in this paper.

CRediT Author Statement

Omar H. Salman: Responsible for methodology, conceptualization, designing the algorithms, simulation and writing the original draft. Mohammed I. Aal-Nouman: his task was visualization and investigation the state-of-the-art related research works. Zahraa K. Taha: Responsible for software development, data curation, and writing the article. Muntadher Q. Alsabah: Responsible for proofreading the paper and improve the English writing of our manuscript. Yaseein S. Hussein: his task was to review the paper and provide some useful comments regarding the paper organization and development. Zahraa Adnan: Responsible for reviewing the dataset tables, gathering related information, and providing technical comments regarding the features’ extraction.

Declaration of Competing Interest

The authors would like to declare that there are no competing financial interests or personal relationships which have, or could be perceived to have, influenced the work presented in this paper.
SubjectEmergency medicine
Specific subject areaTriaging, classifying, prioritizing patients, medical services in emergency departments (Eds), remote patients in telemedicine, and E-health monitoring systems.
Type of dataExcel spread sheets, table.
How data has been obtainedThe ECG, blood pressure, and SpO2 signals have been collected and downloaded from the online library Physionet [1]. We have applied some signal processing algorithms in order to extract the essential features of the datasets. The algorithms have been implemented and simulated in a real-time software environment. The outcomes of the simulated data are organized, structured, formulated and presented as multi diseases dataset.
Data formatRaw and analysed.
Parameters used in the data collectionParameters include medical essential features such as: QRS width; ST elevation; Peaks number and cycle interval from ECG signal; SpO2 level from SpO2 signal; high Blood (systolic) pressure value; and low-pressure (diastolic) value from blood pressure signal. In addition, text-inputs parameters are collected as input data, which are represented by chest pain, shortness of breath, palpitation and whether patient at rest or not.
Description of the collected dataSimulation SetupThe ECG, blood pressure, and SpO2 signals have been collected from the online library Physionet [1]. Each signal has more than 2000 elements. Each element in the signal has two values. The first value represents the time and the second represents voltage. The array of each signal has two columns where each column is represented by a value. The number of rows is defined by the number of elements in the signal, which starts from (0) and it ends at (n). A real time data processing algorithm is used to extract the required features. We have implemented our algorithms in different simulation environments. The proposed algorithms are implemented using the JAVA software programming language. Moreover, cross-platform, Apache, MySQL, PHP, and Perl, known as (XAMMP), is used in this paper. Based on our simulation, the dataset is re-organized and re-formatted to be presented in a structure dataset format. In addition, the dataset is re-presented using tables. The dataset illustrates the patient's profile and the type of diseases.
Data source locationWe describe simulated data accompanied by codes. However, the required information for the database sources is provided in PhysioNet, which is a repository of freely available medical research data that is managed by the Massachusetts Institute of Technology (MIT) Laboratory for computational physiology [1].City: BostonCountry: United States of America (USA)Laboratory for computational physiologyMIT, E25–50545 Carleton St.Cambridge, MA 02139
Data accessibilityThe primary data sources are available in a public repository and given in[1] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley, and P. C. Ivanov, “PhysioBank, PhysioToolkit, and PhysioNet: Component of a New Research Resource for Complex Physiologic Signals,” Circulation, vol. 101, pp. E215–20, 2000, Direct URL to data: https://physionet.org/Simulated data accompanied with codes are presented in https://data.mendeley.com/datasets/22d2kcr2yp/1
Related research article[2] O. H. Salman, M. Aal-Nouman, Z. K. Taha, Reducing Waiting Time for Remote Patients in Telemedicine with Considering Treated Patients in Emergency Department Based on Body Sensors Technologies and Hybrid Computational Algorithms: Toward Scalable and Efficient Real Time Healthcare Monitoring System. DOI: 10.1016/j.jbi.2020.103592
  7 in total

1.  PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.

Authors:  A L Goldberger; L A Amaral; L Glass; J M Hausdorff; P C Ivanov; R G Mark; J E Mietus; G B Moody; C K Peng; H E Stanley
Journal:  Circulation       Date:  2000-06-13       Impact factor: 29.690

2.  Detection of fetal heart rate through 3-D phase space analysis from multivariate abdominal recordings.

Authors:  Evaggelos C Karvounis; Markos G Tsipouras; Dimitrios I Fotiadis
Journal:  IEEE Trans Biomed Eng       Date:  2009-02-18       Impact factor: 4.538

3.  Multi-sources data fusion framework for remote triage prioritization in telehealth.

Authors:  O H Salman; M F A Rasid; M I Saripan; S K Subramaniam
Journal:  J Med Syst       Date:  2014-07-22       Impact factor: 4.460

Review 4.  Systematic Review of Real-time Remote Health Monitoring System in Triage and Priority-Based Sensor Technology: Taxonomy, Open Challenges, Motivation and Recommendations.

Authors:  O S Albahri; A S Albahri; K I Mohammed; A A Zaidan; B B Zaidan; M Hashim; Omar H Salman
Journal:  J Med Syst       Date:  2018-03-22       Impact factor: 4.460

5.  Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related "Big Data" Using Body Sensors information and Communication Technology.

Authors:  Naser Kalid; A A Zaidan; B B Zaidan; Omar H Salman; M Hashim; H Muzammil
Journal:  J Med Syst       Date:  2017-12-29       Impact factor: 4.460

6.  Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization "Large Scales Data" Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology.

Authors:  Naser Kalid; A A Zaidan; B B Zaidan; Omar H Salman; M Hashim; O S Albahri; A S Albahri
Journal:  J Med Syst       Date:  2018-03-02       Impact factor: 4.460

7.  Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: Toward scalable and efficient real time healthcare monitoring system.

Authors:  Omar Hussein Salman; Mohammed Imad Aal-Nouman; Zahraa K Taha
Journal:  J Biomed Inform       Date:  2020-10-19       Impact factor: 6.317
  7 in total

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