Electrocardiograms (ECGs) captured by wearable ECG devices easily contain artifacts because of measurement faults. Since the frequency characteristics of artifacts are quite similar to those of R waves, it may result in R-R interval (RRI) miscalculations. To enable accurate heart rate variability (HRV) analysis in daily life, this paper proposes a method to reliably evaluate RRI measurement status, one that uses the electric potential characteristics of the QRS complex. Initial evaluation results show that it has the potential to distinguish miscalculated RRIs. Also proposed is a new RRI outlier exclusion method that uses the aforementioned method. Time domain measures of HRV derived from the proposed RRI outlier exclusion method are found to be more accurate than those derived from the conventional one.
Electrocardiograms (ECGs) captured by wearable ECG devices readily contain artifacts due to measurement faults. Since artifacts and R waves have quite similar frequency characteristics, R wave misdetection or R-R interval (RRI) miscalculation may result. Aiming at accurate analysis of heart rate variability (HRV), this paper proposes a new RRI outlier processing method consisting of three steps: evaluating RRI reliability, excluding RRI outlier, and complementing missing RRI. In the rst step, the method evaluates the measurement status of all detected R waves and calculates RRI reliability based on the measurement status of a combination of the measurement status of two R waves. Since we target wearable ECG devices used in non-medical environment, the method evaluates R waves based on the threshold electric potential for left ventricular hypertrophy, and determines those exceeding the threshold as artifacts. The method accordingly sets lower reliability to RRIs containing R waves evaluated as artifacts. In the second step, the method excludes all RRIs with low reliability as outliers. These steps may be effective for HRV measures in the time domain, but are not suf cient for analyzing HRV measures in the frequency domain. Resampling the time series RRI data, which is essential for analyzing HRV in the frequency domain, may produce outliers if the target RRIs contain missing values. Our method accordingly complements missing RRIs before data resampling based on RRI characteristics. We postulate that consecutive changes in RRIs follow a simple formula consisting of three components: direct current, low frequency, and high frequency. Our method complements missing values according to the formula, which is calculated from RRIs time series regarded as having been properly measured. To con rm the effectiveness of the method before applying it to ECGs recorded by wearable devices, we evaluated all the steps using pseudo-ECGs generated arti cially by adding noise and artifacts to open ECG data. Initial evaluation results showed that the proposed method outperformed conventional method regarding the precision of both time and frequency domain measures of HRV.
To improve patients’ adherence to continuous positive airway pressure (CPAP) therapy, this study aimed to clarify whether machine learning-based data analysis can identify the factors related to poor CPAP adherence (i.e., CPAP usage that does not reach four hours per day for five days a week). We developed a CPAP adherence prediction model using logistic regression and learn-to-rank machine learning with a pairwise approach. We then investigated adherence prediction performance targeting a 12-week period and the top ten factors correlating to poor CPAP adherence. The CPAP logs of 219 patients with obstructive sleep apnea (OSA) obtained from clinical treatment at Kyoto University Hospital were used. The highest adherence prediction accuracy obtained was an F1 score of 0.864. Out of the top ten factors obtained with the highest prediction accuracy, four were consistent with already-known clinical knowledge. The factors for better CPAP adherence indicate that air leakage should be avoided, mask pressure should be kept constant, and CPAP usage duration should be longer and kept constant. The results indicate that machine learning is an adequate method for investigating factors related to poor CPAP adherence.
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