Prediction of heart abnormalities using deep learning model and wearabledevices in smart health homes

Abstract

The prediction of abnormality in the heart functionality at an early stage increases the chances of saving the life of people. Thus, this paper proposes a technique which predicts the abnormality in the functionality of the heart using heart rate in the form of beats per minute using wearable devices and deep learning model. The devices used are wrist strap and devices that can be fixed near the chest of the person or back of the person where heart beat can be detected. The proposed system is divided into 3 modules: (1) data collection and processing module, (2) prediction module and (3) communication module. First module is used to collect data and process, while prediction module predicts the abnormal functionality of the heart using deep learning model. One of the advantage of the proposed work in this paper is communication module as the communication is given to the doctor who can perform analysis before the patient reaches the hospital. A message is sent to the ambulance so that it reaches the destination on time. The message related to first aid is sent to two dear ones and the patient such that appropriate measures can be taken. The proposed technique is evaluated in terms of sensitivity, specificity, F 1 -Score, time and ROC curve metrics. It is also compared with the Two Stage Neural Network and TSNN and proved to be performing better.

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

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Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

“…Feature selection is then proceeded by the classification process which classifies the face image in terms of age and gender. Age is categorized into 8 classes as (0-2), (4-6), (8)(9)(10)(11)(12), (15)(16)(17)(18)(19)(20), (25)(26)(27)(28)(29)(30)(31)(32), (38)(39)(40)(41)(42)(43), (48)(49)(50)(51)(52)(53), and (60-100), and gender is categorized as male/female. Figure 7 shows the result of classification.…”

“…DBN learning is quicker than DNN due to the inclusion of RBM. The RBMs are stacked DBM with unguided connections across the levels [48][49][50][51][52][53].…”

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

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