Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 19; p. 6229
• Main Authors: Baty, Florent, Cvetkovic, Dragan, Boesch, Maximilian, Bauer, Frederik, Adão Martins, Neusa R, Rossi, René M, Schoch, Otto D, Annaheim, Simon, Brutsche, Martin H
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.09.2024; MDPI
• Subjects: Accuracy; Adult; Aged; Algorithms; Electrocardiogram; Electrocardiography; Electrocardiography - instrumentation; Electrocardiography - methods; Electrodes; Female; Fourier transforms; Heart beat; Heart rate; Heart Rate - physiology; Humans; Laboratories; Light emitting diodes; Male; Measurement; Middle Aged; monitoring belt; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; multi-sensor; Patient monitoring equipment; Patients; Polyesters; polysomnography; Polysomnography - instrumentation; Polysomnography - methods; Respiration; Respiratory Rate - physiology; ROC Curve; Sensors; Sleep apnea; Sleep apnea syndromes; Sleep Apnea Syndromes - diagnosis; Sleep Apnea Syndromes - physiopathology; Support Vector Machine; Textiles; Time series; wearable; Wearable Electronic Devices; Switzerland; wearable; polysomnography; monitoring belt; multi-sensor; sleep apnea
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24196229

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.