Unobtrusive Nocturnal Heartbeat Monitoring by a Ballistocardiographic Sensor in Patients with Sleep Disordered Breathing.
Matthias Daniel ZinkChristoph BrüserBjörn-Ole StübenAndreas NappRobert StöhrSteffen LeonhardtNikolaus MarxKarl MischkeJörg Bernhard SchulzJohannes SchieferPublished in: Scientific reports (2017)
Sleep disordered breathing (SDB) is known for fluctuating heart rates and an increased risk of developing arrhythmias. The current reference for heartbeat analysis is an electrocardiogram (ECG). As an unobtrusive alternative, we tested a sensor foil for mechanical vibrations to perform a ballistocardiography (BCG) and applied a novel algorithm for beat-to-beat cycle length detection. The aim of this study was to assess the correlation between beat-to-beat cycle length detection by the BCG algorithm and simultaneously recorded ECG. In 21 patients suspected for SDB undergoing polysomnography, we compared ECG to simultaneously recorded BCG data analysed by our algorithm. We analysed 362.040 heartbeats during a total of 93 hours of recording. The baseline beat-to-beat cycle length correlation between BCG and ECG was r s = 0.77 (n = 362040) with a mean absolute difference of 15 ± 162 ms (mean cycle length: ECG 923 ± 220 ms; BCG 908 ± 203 ms). After filtering artefacts and improving signal quality by our algorithm, the correlation increased to r s = 0.95 (n = 235367) with a mean absolute difference in cycle length of 4 ± 72 ms (ECG 920 ± 196 ms; BCG 916 ± 194 ms). We conclude that our algorithm, coupled with a BCG sensor foil provides good correlation of beat-to-beat cycle length detection with simultaneously recorded ECG.
Keyphrases
- heart rate
- heart rate variability
- mass spectrometry
- blood pressure
- multiple sclerosis
- ms ms
- machine learning
- deep learning
- obstructive sleep apnea
- neural network
- end stage renal disease
- heart failure
- label free
- ejection fraction
- real time pcr
- newly diagnosed
- chronic kidney disease
- quality improvement
- big data
- atrial fibrillation
- pulmonary embolism
- sleep apnea
- quantum dots
- electronic health record
- congenital heart disease
- patient reported outcomes
- data analysis