scholarly journals Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3472 ◽  
Author(s):  
D’Mello ◽  
Skoric ◽  
Xu ◽  
Roche ◽  
Lortie ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Time Algorithm ◽  
Cardiac Monitoring ◽  
Computational Time ◽  
Heart Rate Monitoring ◽  
Heart Rates ◽  
Non Invasive ◽  
Beat Detection ◽  
Speed Accuracy

Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG–GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36–140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

An Effective Algorithm for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms

2020 ◽  
Vol 10 (3) ◽  
pp. 633-640 ◽  
Author(s):  
Jingxian Liang ◽  
Jialin Huang ◽  
Liwei Mu ◽  
Baoxian Yu ◽  
Pengbin Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Cross Correlation ◽  
Effective Algorithm ◽  
Sleep Staging ◽  
Heart Rate Monitoring ◽  
Non Invasive ◽  
Step Procedure ◽  
The Mean ◽  
Beat Detection ◽  
Beat Heart

Ballistocardiograms (BCG) is an essential signal for vital sign monitoring. Obtaining the beat-to-beat intervals from BCG signal is of great significance for home-care applications, such as sleep staging, heart disease alerting, etc. The current approaches of detecting beat-to-beat intervals from BCG signals are complex. In this paper, we develop a non-invasive BCG monitoring system, and propose an effective and accurate algorithm for beat-to-beat detection. Firstly, a heartbeat shape is adaptively modeled based on a two-step procedure by taking advantage of the J-peak and the K-valley of BCG signals. Then, forward and backward detections with the criteria of both the morphological distance and the cross-correlation are jointly employed to find the position of each BCG signal, and in turn, to determine the beat-to-beat intervals of BCG signals. The proposed method was validated in at least 90 minutes recording from 10 subjects in various setups. The mean absolute beat-to-beat intervals error was 10.72 ms and on an average 97.93% of the beat-to-beat intervals were detected.

scholarly journals Characterisation of Textile Embedded Electrodes for Use in a Neonatal Smart Mattress Electrocardiography System

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 999
Author(s):  
Henry Dore ◽  
Rodrigo Aviles-Espinosa ◽  
Zhenhua Luo ◽  
Oana Anton ◽  
Heike Rabe ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Intensive Care ◽  
High Resolution ◽  
Neonatal Intensive Care Unit ◽  
Real Time ◽  
Neonatal Intensive Care ◽  
Delivery Room ◽  
Proof Of Concept ◽  
Heart Rate Monitoring

Heart rate monitoring is the predominant quantitative health indicator of a newborn in the delivery room. A rapid and accurate heart rate measurement is vital during the first minutes after birth. Clinical recommendations suggest that electrocardiogram (ECG) monitoring should be widely adopted in the neonatal intensive care unit to reduce infant mortality and improve long term health outcomes in births that require intervention. Novel non-contact electrocardiogram sensors can reduce the time from birth to heart rate reading as well as providing unobtrusive and continuous monitoring during intervention. In this work we report the design and development of a solution to provide high resolution, real time electrocardiogram data to the clinicians within the delivery room using non-contact electric potential sensors embedded in a neonatal intensive care unit mattress. A real-time high-resolution electrocardiogram acquisition solution based on a low power embedded system was developed and textile embedded electrodes were fabricated and characterised. Proof of concept tests were carried out on simulated and human cardiac signals, producing electrocardiograms suitable for the calculation of heart rate having an accuracy within ±1 beat per minute using a test ECG signal, ECG recordings from a human volunteer with a correlation coefficient of ~ 87% proved accurate beat to beat morphology reproduction of the waveform without morphological alterations and a time from application to heart rate display below 6 s. This provides evidence that flexible non-contact textile-based electrodes can be embedded in wearable devices for assisting births through heart rate monitoring and serves as a proof of concept for a complete neonate electrocardiogram monitoring system.

Detecting Pedestrian Situation Awareness in Real-Time: Algorithm Development Using Heart Rate Variability and Phone Position

2018 ◽  
Vol 62 (1) ◽  
pp. 1579-1583
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Yi Wang ◽  
Si Yang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Situation Awareness ◽  
Mental State ◽  
Time Algorithm ◽  
Support Vector ◽  
Vehicle Design ◽  
Automated Vehicle ◽  
Hrv Analysis

To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.

A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms

2016 ◽  
Vol 37 (11) ◽  
pp. 1885-1909 ◽  
Author(s):  
Mojtaba Jafari Tadi ◽  
Eero Lehtonen ◽  
Tero Hurnanen ◽  
Juho Koskinen ◽  
Jonas Eriksson ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Hilbert Transform ◽  
Heart Rate Monitoring

A Real-time Algorithm for Obstacle Avoidance of Autonomous Mobile Robots

Robotica ◽  
1992 ◽  
Vol 10 (3) ◽  
pp. 217-227 ◽  
Author(s):  
Huang Han-Pang ◽  
Lee Pei-Chien
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Time Algorithm ◽  
Autonomous Mobile Robots ◽  
Computational Time ◽  
Avoidance Problem

SUMMARYA real-time obstacle avoidance algorithm is proposed for autonomous mobile robots. The algorithm is sensor-based and consists of a H-mode and T-mode. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. It will be shown that the algorithm is more efficient and more robust than other sensor-based algorithms. In addition, the algorithm will guarantee a solution for the obstacle avoidance problem. Since the algorithm only takes up a small computational time, it can be implemented in real time.

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