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.

We present a novel seismocardiography (SCG)based approach for real-time cardio-respiratory activity measurement called the Autocorrelated Differential Algorithm (ADA). Measurements were performed on ten male subjects in the supine position for three 7-minute-long sets each, corresponding to 14,619 heartbeats. The ADA utilized temporal variations, windowing, and autocorrelation to produce physiological measurements corresponding to heart rate (HR), and left ventricular ejection time, and estimations of respiration rate, volume, and phase. The versatility of the ADA was investigated in two contexts: physical exertion and heart rate variability. The accuracy of HR measurements at a sampling frequency of 200 Hz resulted in a correlation coefficient (r 2) of 0.9808 when compared with a manual annotation of all datasets. Its reproducibility was tested on externally obtained SCG and electrocardiography datasets, which produced an r 2 of 0.8224. The accuracy and computational time were also characterized by different sampling frequencies to quantify performance. The recommended sampling frequency is 200 Hz corresponding to a computation time of 0.05 s per instantaneous measurement using a standard desktop computer. The ADA delivered real-time SCG measurements with a refresh rate that was dependent on the computational time per measurement, which could be decreased by lowering the sampling frequency. The presented algorithm offers a novel tool toward real-time physiological monitoring in clinical and everyday scenarios.

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