Snoring (SN) is an early sign of upper airway dysfunction, and it is strongly associated with obstructive sleep apnea (OSA). SN detection is important to monitor SN objectively and to improve the diagnostic sensitivity of sleep-disordered breathing (SDB). In this study, an automatic snore detection method using an Emfit (Electromechanical film transducer) signal is presented. Representative polysomnographs of normal breathing (NB) and SN periods from 30 subjects were selected. Individual SN events were identified using source separation applying nonnegative matrix factorization deconvolution (NMFD). The algorithm was evaluated using manual annotation of the polysomnographic recordings. According to our results, the sensitivity (Se), and the positive predictive value (PPV) of the developed method to reveal snoring from the Emfit signal were 82.81% and 86.29%, respectively. Compared to other approaches, our method adapts to the individual spectral snoring profile of the subject rather than matching a particular spectral profile, estimates the snoring intensity, and obtains the specific spectral profile of the snores in the epoch. Additionally, no training is necessary. This study suggests that it is possible to detect individual SN events with Emfit mattress, which can be used as a contactless alternative to more conventional methods such as piezo-snore sensors or microphones.

The aim of this study is to explore the capability of an Emfit (electromechanical film transducer) mattress to detect snoring (SN) by analyzing the spectral differences between normal breathing (NB) and SN. Episodes of representative NB and SN of a maximum of 10 min were visually selected for analysis from 33 subjects. To define the bands of interest, we studied the statistical differences in the power spectral density (PSD) between both breathing types. Three bands were selected for further analysis: 6–16 Hz (BW1), 16–30 Hz (BW2) and 60–100 Hz (BW3). We characterized the differences between NB and SN periods in these bands using a set of spectral features estimated from the PSD. We found that 15 out of the 29 features reached statistical significance with the Mann–Whitney U-test. Diagnostic properties for each feature were assessed using receiver operating characteristic analysis. According to our results, the highest diagnostic performance was achieved using the power ratio between BW2 and BW3 (0.85 area under the receiver operating curve, 80% sensitivity, 80% specificity and 80% accuracy). We found that there are significant differences in the defined bands between the NB and SN periods. A peak was found in BW3 for SN epochs, which was best detected using power ratios. Our work suggests that it is possible to detect snoring with an Emfit mattress. The mattress-type movement sensors are inexpensive and unobtrusive, and thus provide an interesting tool for sleep research.

The impact of tissue movements on the accuracy of heart rate (HR) estimates is a challenge in today’s wearable technology. Tissue movements are caused by muscle activity that modifies the optical path of the reflectance photoplethysmography (PPG), leading to motion artifacts (MA) that mask the true HR. This kind of MA is not always detected using accelerometers (ACC). In this study, we propose a method to increase the PPG HR accuracy of a wristwatch using wrist surface electromyogram (EMG) and ACC using spectrum subtraction algorithms. We collected the wrist EMG, wristwatch PPG, ACC data, and the ECG from nine subjects. Data were recorded during four frequent hand movements and three activities (weightlifting and running/walking with and without holding a racket). The added value of the EMG was studied. Visual results indicate that wrist EMG correlates well with the MA seen in the PPG signal and provides additional information over the typically used ACC data. Our analysis showed that the proposed artifact removal method using EMG and ACC decreases the HR estimation error on average by 49% compared to only ACC. Our results showed that wrist EMG contains complementary information on the PPG artifacts and offers a novel signal modality for improving optical HR estimation accuracy in smartwatches.

Introduction: Unobtrusive bed mattress sensors such us the electromechanical film transducer (Emfit) record the heart's activity, breathing, and body movements with clinical applications ranging from epilepsy to sleep disorders. The mechanical activity of the heart recorded using these sensors is known as ballistocardiogram (BCG). However, BCG shape changes on position, patient, and mattress. Aim: In this study, we isolate a position- independent heart signal designed to retrieve the heart activity recorded using an Emfit mattress sensor using extended polysomnography (PSG) recordings as a reference. Methods: We used spectral and source separation techniques to infer the heart's Emfit Pulse (EP). We validated the resulting signal using representative 10- minute normal breathing epochs extracted from the PSG from 33 subjects and by estimating the heart rate (HR) from the EP and compared it against the electrocardiogram (ECG) using non-overlapping one- minute windows. Results: Results show a signal similar in shape to a photoplethysmogram (PPG) with different timings in relation to the ECG's R-peak. We found good agreement between HR with a mean absolute error (MAE) of 2.4 beats per minute (bpm) with standard deviation of 4.6bpm. Conclusions: A position independent heart pulse signal from the Emfit mattress sensor was obtained in a range of subjects and validated by means of HR analysis.