Literature DB >> 28391210

Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone.

Olli Lahdenoja, Tero Hurnanen, Zuhair Iftikhar, Sami Nieminen, Timo Knuutila, Antti Saraste, Tuomas Kiviniemi, Tuija Vasankari, Juhani Airaksinen, Mikko Pankaala, Tero Koivisto.   

Abstract

We present a smartphone-only solution for the detection of atrial fibrillation (AFib), which utilizes the built-in accelerometer and gyroscope sensors [inertial measurement unit, (IMU)] in the detection. Depending on the patient's situation, it is possible to use the developed smartphone application either regularly or occasionally for making a measurement of the subject. The smartphone is placed on the chest of the patient who is adviced to lay down and perform a noninvasive recording, while no external sensors are needed. After that, the application determines whether the patient suffers from AFib or not. The presented method has high potential to detect paroxysmal ("silent") AFib from large masses. In this paper, we present the preprocessing, feature extraction, feature analysis, and classification results of the envisioned AFib detection system based on clinical data acquired with a standard mobile phone equipped with Google Android OS. Test data was gathered from 16 AFib patients (validated against ECG), as well as a control group of 23 healthy individuals with no diagnosed heart diseases. We obtained an accuracy of 97.4% in AFib versus healthy classification (a sensitivity of 93.8% and a specificity of 100%). Due to the wide availability of smart devices/sensors with embedded IMU, the proposed methods could potentially also scale to other domains such as embedded body-sensor networks.

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Year:  2017        PMID: 28391210     DOI: 10.1109/JBHI.2017.2688473

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  18 in total

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4.  Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study.

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5.  Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables.

Authors:  Mojtaba Jafari Tadi; Eero Lehtonen; Antti Saraste; Jarno Tuominen; Juho Koskinen; Mika Teräs; Juhani Airaksinen; Mikko Pänkäälä; Tero Koivisto
Journal:  Sci Rep       Date:  2017-07-28       Impact factor: 4.379

6.  Multiclass Classifier based Cardiovascular Condition Detection Using Smartphone Mechanocardiography.

Authors:  Zuhair Iftikhar; Olli Lahdenoja; Mojtaba Jafari Tadi; Tero Hurnanen; Tuija Vasankari; Tuomas Kiviniemi; Juhani Airaksinen; Tero Koivisto; Mikko Pänkäälä
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8.  Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.

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Journal:  Sensors (Basel)       Date:  2020-06-24       Impact factor: 3.576

9.  Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set.

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Journal:  Sensors (Basel)       Date:  2021-05-19       Impact factor: 3.576

10.  Novel Density Poincaré Plot Based Machine Learning Method to Detect Atrial Fibrillation From Premature Atrial/Ventricular Contractions.

Authors:  Syed Khairul Bashar; Dong Han; Fearass Zieneddin; Eric Ding; Timothy P Fitzgibbons; Allan J Walkey; David D McManus; Bahram Javidi; Ki H Chon
Journal:  IEEE Trans Biomed Eng       Date:  2021-01-20       Impact factor: 4.538

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