A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals

Abstract

This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications. The framework includes two stages, i.e. heartbeat identification and refinement, respectively. In the first stage, an adaptive threshold-based auto-segmentation approach is proposed to select out heartbeat candidates, including the real heartbeats and large amounts of motion-artifact-induced interferential spikes. Then twenty-six features are extracted for each candidate in time, spatial, frequency and statistical domains, and evaluated by a spare support vector machine (SVM) to select out ten critical features which can effectively reveal residual heartbeat information. Afterwards, an SVM model, created on the training data using the selected feature set, is applied to find high confident heartbeats from a large number of candidates in the testing data. In the second stage, the SVM classification results are further refined by two steps: (1) a rule-based classifier with two attributes named 'continuity check' and 'locality check' for outlier (false positives) removal, and (2) a heartbeat interpolation strategy for missing-heartbeat (false negatives) recovery. The framework is evaluated on a wrist-ECG dataset acquired by a semi-customized platform and also a public dataset. When the signal-to-noise ratio is as low as -7 dB, the mean absolute error of the estimated IHR is 1.4 beats per minute (BPM) and the root mean square error is 6.5 BPM. The proposed framework greatly outperforms well-established approaches, demonstrating that it can effectively identify the heartbeats from ECG signals continuously corrupted by intense motion artifacts and robustly estimate the IHR. This study is expected to contribute to robust long-term wearable IHR monitoring for pervasive heart health and fitness management.

Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals

“…The customized hardware platform comprises two parts, i.e., the ECG signal [ 5 ] and PPG signal acquisition subsystems, as shown in the right part of Fig. 1 a.…”

“…1 b. To robustly identify heartbeats from the weak arm-ECG signal, our previously reported machine learning-enabled framework (MLEF) is applied [ 5 ], which can effectively identify corrupted heartbeats and robustly estimate the heart rate from the wrist-ECG signals based on the support vector machine (SVM) classifier, even with an SNR as low as −7 dB. This heartbeat identification framework includes the following four steps:…”

“…In the second step, ten critical motion artifacts-tolerant features are extracted from multiple domains for each candidate, include R angle (angle of the R peak) , S angle (angle of the S valley) , RS Diff (voltage difference between the R peak and the S valley) , R Symmetry (the symmetry of the R peak) , S Symmetry (the symmetry of the S valley) , SKNS (skewness of the R peak region) , VAR (variance of the R peak region) , RMS (root mean square of the R peak region) , alpha - 3 (angle of the slop of the third sample on the left side of the R peak), and alpha 2 (angle of the slop of the second sample on the right side of the R peak). These features are selected from twenty-six raw features by a sparse SVM which can effectively push the non-significant features towards zero [ 5 ].…”

“…The single-arm-PPG signal is also acquired by placing the sensor close to the ECG electrodes for a good wearability. Afterwards, to recognize the heartbeats from the weak ECG signal with many interferential spikes induced by motion artifacts and electromyography (EMG) noise, a machine learning-enabled framework is introduced [ 5 ]. Based on the identified heartbeats in the ECG signal, the heartbeat pairs in the PPG signal are then determined to obtain the PTT measurements which will be used to build the systolic BP (SBP) model (we take special interest in SBP monitoring in this study).…”

A Novel Adaptive Noise Elimination Algorithm in Long RR Interval Sequences for Heart Rate Variability Analysis

“…To summarize, there are algorithms for ectopic beats correction [ 25 , 32 , 33 , 34 , 35 ], restoration of missing heartbeats [ 36 ], and noise elimination [ 37 ]; however, an entirely different picture is seen in which heart rate registrations occur alongside the other processes (physiological and non-physiological). In this instance, it is necessary to maintain a precise synchronization of each RRI with other measurable processes.…”