Lightweight heartbeat detection algorithm for consumer grade wearable ECG measurement devices and its implementation

Matsuura, N.; Kuwabara, Kei; Ogasawara, Takayuki

doi:10.1109/embc48229.2022.9871514

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…We briefly summarize this calculation process. The heart rate was calculated every minute using R-R (R-wave peak to R-wave peak) intervals detected in the ECG in the transmitter ( Matsuura et al, 2022 ). Percent heart rate reserve (%HRR) is a well-known indicator that correlates with the amount of activity or exercise in stroke rehabilitation ( Nathoo et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Ogasawara,

Mukaino,

Matsunaga

et al. 2024

Front. Bioeng. Biotechnol.

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Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.

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Section: Methodsmentioning

confidence: 99%

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Ogasawara,

Mukaino,

Matsunaga

et al. 2024

Front. Bioeng. Biotechnol.

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“…2 gives an overview of the procedures for R wave detection and RRI correction for the validation ECG signals. We used the R wave detection algorithm [23] based on the time differential of the ECG signal, which is the target of this study, to detect peaks derived from R waves in ECG signals generated using ECGSYN. Based on the fact that QRS duration is almost constant in people without cardiorespiratory diseases, the algorithm selectively makes the QRS-induced peak sensitive to the specific time width determined by considering the nature of an ECG, and then performs R wave detection based on the ECG time difference values.…”

Section: B Demonstration Of Compensation Methodsmentioning

confidence: 99%

“…For each acquired ECG data, we applied a 15th order FIR high pass and low pass filter with cutoff frequencies of 10 Hz and 40 Hz to remove low-frequency body movement noise, and then calculated RRI using 3 different methods. First, RRI was calculated for the filtered ECG signals by the algorithm used in the previous section [23]. Second, the RRI was calculated by the algorithm and corrected by the proposed method after sampling to 125 Hz for the filtered ECG.…”

Section: Experimental Testmentioning

confidence: 99%

Lightweight and High Accurate RR Interval Compensation for Signals From Wearable ECG Sensors

Hashimoto

2024

IEEE Sens. Lett.

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ECG and EMG Monitoring with Smart Textile hitoe™

Yamaguchi,

Togo

2024

Wearable Biosensing in Medicine and Healthcare

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Lightweight heartbeat detection algorithm for consumer grade wearable ECG measurement devices and its implementation

Cited by 4 publications

References 28 publications

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Lightweight and High Accurate RR Interval Compensation for Signals From Wearable ECG Sensors

ECG and EMG Monitoring with Smart Textile hitoe™

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