OBJECTIVES: This study tested the feasibility of automatically detecting advanced sleep disordered breathing (SDB) from a pacemaker trans-thoracic impedance sensor. BACKGROUND: SDB is prevalent yet under-diagnosed in patients with cardiovascular disease. The potential for automated detection of SDB in patients receiving pacemakers with respiration sensors has not been fully explored. We hypothesized that the trans-thoracic impedance sensor could be utilized for automatic detection of advanced SDB. METHODS: Patients underwent overnight polysomnography (PSG). The pacemaker trans-thoracic impedance signal was simultaneously recorded and time synchronized with the polysomnograph. Cardiovascular health variables were abstracted from medical records. Apnea was defined as cessation of inspiratory airflow lasting 10 seconds or longer. Hypopnea was defined as a reduction of tidal volume of at least 30% from baseline tidal volume, lasting 10 seconds or more. A computer algorithm (PM-A) was developed to automatically detect SDB from the pacemaker impedance sensor data. The performance of automated SDB detection was compared against PSG. RESULTS: Sixty patients (aged 69 +/- 12 years, 45 males) were studied. Advanced SDB (moderate or severe) was diagnosed in 40 patients. Severe SDB (apnea-hypopnea index [AHI]> or = 30) was diagnosed in 32 patients (53%), but only 5 patients had prior diagnosis of the disease. Moderate SDB (30 > AHI > 15) was diagnosed in 8 patients of whom only two were previously diagnosed. Cardiovascular health variables did not predict the presence of advanced SDB. PM-A derived AHI correlated with that of the PSG (r = 0.80, P < 0.01). The algorithm identified patients with advanced SDB with 82% sensitivity and 88% specificity. CONCLUSIONS: It is feasible to automatically measure SDB severity using a pacemaker trans-thoracic impedance sensor. Advanced SDB was frequently undiagnosed in this cohort of pacemaker patients.
OBJECTIVES: This study tested the feasibility of automatically detecting advanced sleep disordered breathing (SDB) from a pacemaker trans-thoracic impedance sensor. BACKGROUND: SDB is prevalent yet under-diagnosed in patients with cardiovascular disease. The potential for automated detection of SDB in patients receiving pacemakers with respiration sensors has not been fully explored. We hypothesized that the trans-thoracic impedance sensor could be utilized for automatic detection of advanced SDB. METHODS:Patients underwent overnight polysomnography (PSG). The pacemaker trans-thoracic impedance signal was simultaneously recorded and time synchronized with the polysomnograph. Cardiovascular health variables were abstracted from medical records. Apnea was defined as cessation of inspiratory airflow lasting 10 seconds or longer. Hypopnea was defined as a reduction of tidal volume of at least 30% from baseline tidal volume, lasting 10 seconds or more. A computer algorithm (PM-A) was developed to automatically detect SDB from the pacemaker impedance sensor data. The performance of automated SDB detection was compared against PSG. RESULTS: Sixty patients (aged 69 +/- 12 years, 45 males) were studied. Advanced SDB (moderate or severe) was diagnosed in 40 patients. Severe SDB (apnea-hypopnea index [AHI]> or = 30) was diagnosed in 32 patients (53%), but only 5 patients had prior diagnosis of the disease. Moderate SDB (30 > AHI > 15) was diagnosed in 8 patients of whom only two were previously diagnosed. Cardiovascular health variables did not predict the presence of advanced SDB. PM-A derived AHI correlated with that of the PSG (r = 0.80, P < 0.01). The algorithm identified patients with advanced SDB with 82% sensitivity and 88% specificity. CONCLUSIONS: It is feasible to automatically measure SDB severity using a pacemaker trans-thoracic impedance sensor. Advanced SDB was frequently undiagnosed in this cohort of pacemaker patients.
Authors: Lien Desteghe; Jeroen M L Hendriks; R Doug McEvoy; Ching Li Chai-Coetzer; Paul Dendale; Prashanthan Sanders; Hein Heidbuchel; Dominik Linz Journal: Clin Res Cardiol Date: 2018-04-12 Impact factor: 5.460
Authors: Girardin Jean-Louis; Ferdinand Zizi; Luther T Clark; Clinton D Brown; Samy I McFarlane Journal: J Clin Sleep Med Date: 2008-06-15 Impact factor: 4.062
Authors: Domenico G Della Rocca; Maria Albanese; Fabio Placidi; Giovanni B Forle; Luigi Di Biase; Valentina Ribatti; Luca Santini; Francesca Izzi; Lucia Cicchini; Mariolina Lovecchio; Sergio Valsecchi; Carlo Lavalle; Andrea Natale; Nicola B Mercuri; Andrea Romigi Journal: J Interv Card Electrophysiol Date: 2019-10-23 Impact factor: 1.900
Authors: Henrik Fox; Georg Nölker; Klaus-Jürgen Gutleben; Thomas Bitter; Dieter Horstkotte; Olaf Oldenburg Journal: Herzschrittmacherther Elektrophysiol Date: 2014-02-12