Abstract
Cardiac translational and rotational vibrations induced by left ventricular motions are measurable using joint seismocardiography (SCG) and gyrocardiography (GCG) techniques. Multi-dimensional non-invasive monitoring of the heart reveals relative information of cardiac wall motion. A single inertial measurement unit (IMU) allows capturing cardiac vibrations in sufficient details and enables us to perform patient screening for various heart conditions. We envision smartphone mechanocardiography (MCG) for the use of e-health or telemonitoring, which uses a multi-class classifier to detect various types of cardiovascular diseases (CVD) using only smartphone’s built-in internal sensors data. Such smartphone App/solution could be used by either a healthcare professional and/or the patient him/herself to take recordings from their heart. We suggest that smartphone could be used to separate heart conditions such as normal sinus rhythm (SR), atrial fibrillation (AFib), coronary artery disease (CAD), and possibly ST-segment elevated myocardial infarction (STEMI) in multiclass settings. An application could run the disease screening and immediately inform the user about the results. Widespread availability of IMUs within smartphones could enable the screening of patients globally in the future, however, we also discuss the possible challenges raised by the utilization of such self-monitoring systems.
Highlights
New mobile/portable technologies have the potential to streamline detection and prediction of cardiovascular diseases (CVD) by enabling individuals to monitor themselves via advanced devices such as mobile phones[9], smart watches[10], weighting scale[11], etc
We present a smartphone mechanocardiography (MCG) based solution for autodetection of atrial fibrillation (AFib) and ischemic conditions by considering only mechanical signals through joint seismocardiography (SCG)[28] and gyrocardiography (GCG)[29] signals obtained from built-in accelerometer and gyroscope sensors in smartphones
We presented an approach for classifying multiple heart conditions using well known principles of seismocardiography and gyrocardiography with full analysis of signals derived from a 6-axis smartphone built-in inertial sensor
Summary
Zuhair Iftikhar[1], Olli Lahdenoja[1], Mojtaba Jafari Tadi[1,2], Tero Hurnanen[1], Tuija Vasankari[3], Tuomas Kiviniemi[3], Juhani Airaksinen[3], Tero Koivisto1 & Mikko Pänkäälä[1]. We suggest that smartphone could be used to separate heart conditions such as normal sinus rhythm (SR), atrial fibrillation (AFib), coronary artery disease (CAD), and possibly ST-segment elevated myocardial infarction (STEMI) in multiclass settings. In continuation of that study, we developed an extensive machine learning solution[37] to detect AFib by extracting various features from GCG and SCG signals obtained by only smartphone inertial sensors. The corresponding estimated interbeat duration RR is obtained as RR = ifirst peak /Fs. The algorithm can subsequently return eight cardiac cycles (either R – peak to R – peak or SCG-GCG dominant Peak to Peak) from one signal segment (of length 10 seconds) which are denoted as RR k:1−8. We consider turning point ratios in both input signal denoted as TPR(x) and obtained RR time interval series from the same segment defined as RRITPR = TPR(RRk:[1−8]). There are changes in the signal energy as well as in the overall number of uniform (and non-uniform) patterns during Pre-PCI and STEMI
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