Health-Radio: Towards Contactless Myocardial Infarction Detection Using Radio Signals

Jian, Zhang; Wu, Yuan; Chen, Yanjiao; Chen, Tong

doi:10.1109/tmc.2020.3012681

Cited by 12 publications

(6 citation statements)

References 33 publications

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“…It took several minutes to run the corresponding script on an ordinary PC to process a set of 15 s sampling data. The computational complexity problem also exists in the algorithm in [ 35 ]. It achieved an average error of 12.2 ms for IBI measurement and successfully realized myocardial infarction detection based on HRV.…”

Section: Discussionmentioning

confidence: 99%

“…Most previous studies performed HRV analysis based on the average heart rate over a short time (e.g., 3 s) [ 33 , 34 ]. Empirical mode decomposition (EMD) is another commonly used heartbeat signal extraction algorithm [ 35 , 36 , 37 ] that decomposes the chest wall motion according to the time scale characteristics of the signal itself. However, this method has limitations, such as mode aliasing and end effects.…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

Han

Zhai

Zhang

et al. 2023

Sensors

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Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

Han

Zhai

Zhang

et al. 2023

Sensors

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“…Radar-based high-accuracy movement measurement enables the contactless heart activity detection. In recent publications, some studies are employing the deep learning method, simultaneously collecting radar cardiac cycle and ECG signals to reconstruct ECG signals based on radar signals, subsequently obtaining waveforms similar to electrocardiograms [80], [81]. Some studies also employ deep learning methods to obtain seismocardiograms (SCG) from radar signals [82], [83].…”

Section: B Cardiogram Detection In Clinical Environmentmentioning

confidence: 99%

A Review on Recent Advancements of Biomedical Radar for Clinical Applications

Dong,

Wen,

et al. 2024

IEEE Open J. Eng. Med. Biol.

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The field of biomedical radar has witnessed significant advancements in recent years, paving the way for innovative and transformative applications in clinical settings. Most medical instruments invented to measure human activities rely on contact electrodes, causing discomfort. Thanks to its noninvasive nature, biomedical radar is particularly valuable for clinical applications. A significant portion of the review discusses improvements in radar hardware, with a focus on miniaturization, increased resolution, and enhanced sensitivity. Then, this paper also delves into the signal processing and machine learning techniques tailored for radar data. This review will explore the recent breakthroughs and applications of biomedical radar technology, shedding light on its transformative potential in shaping the future of clinical diagnostics, patient and elderly care, and healthcare innovation.

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“…Day to day fitness activities like bicycle, toe-touch and squat can be detected and monitored using ultrasound-based system [51]. Moreover, totally contactless methods of acquiring Electrocardiogram (ECG) signal and detecting Myocardial Infarction (MI) have also been explored [104,105].…”

Section: Elderly and Patient Carementioning

confidence: 99%

Contactless Human Activity Analysis: An Overview of Different Modalities

Abir

Faisal

Shahid

et al. 2021

Contactless Human Activity Analysis

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Human Activity Analysis (HAA) is a prominent research field in this modern era which has enlightened us with the opportunities of monitoring regular activities or the surrounding environment as per our desire. In recent times, Contactless Human Activity Analysis (CHAA) has added a new dimension in this domain as these systems perform without any wearable device or any kind of physical contact with the user. We have analyzed different modalities of CHAA and arranged them into three major categories: RF-based, sound-based, and vision-based modalities. In this chapter, we have presented state-of-the-art modalities, frequently faced challenges with some probable solutions, and currently used applications of CHAA with future directions.

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Health-Radio: Towards Contactless Myocardial Infarction Detection Using Radio Signals

Cited by 12 publications

References 33 publications

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

A Review on Recent Advancements of Biomedical Radar for Clinical Applications

Contactless Human Activity Analysis: An Overview of Different Modalities

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