Proceedings ArticleDOI
Detecting Heart Anomalies Using Mobile Phones and Machine Learning
Elhoussine Talab,Omar Mohamed,Labeeba Begum,Fadi Aloul,Assim Sagahyroon +4 more
- pp 428-432
Reads0
Chats0
TLDR
A cost-effective and reliable method of diagnosing heart abnormalities by using mobile phones that are nowadays typically available to an average user and shown to perform with an accuracy of 94.2% on the validation set is discussed.Abstract:
One out of four deaths is caused by heart related issues. Acting upon early signs of heart disease can, thus, drastically increase probability of saving lives. This paper discusses a cost-effective and reliable method of diagnosing heart abnormalities by using mobile phones that are nowadays typically available to an average user. A mobile application is developed to detect heart abnormal activities using either a digital stethoscope measurement as input, or a mobile recording of the heart beat using the mobile's microphone. To process the raw heart sound data, we first denoise the signal using wavelet transforms, and then apply machine learning techniques, namely, Convolutional Neural Networks for the classification of the stored heart sounds. A database consisting of recorded human heart sounds and their corresponding diagnosis is used to train the neural network. Moreover, neural network fine-tuning techniques such as ADAM Regularization is used to smoothen the prediction process. The proposed approach is tested on heart sound signals, that are 5 to 8 seconds long, and is shown to perform with an accuracy of 94.2% on the validation set.read more
Citations
More filters
Journal ArticleDOI
Anomaly Detection in Heart Disease Using a Density-Based Unsupervised Approach
Yaser Ahangari Nanehkaran,Zhu Licai,Junde Chen,Ahmed A. M. Jamel,Zhao Shengnan,Yahya Dorostkar Navaei,Mohsen Abdollahzadeh Aghbolagh +6 more
TL;DR: A density-based unsupervised approach to the diagnosis of abnormalities in heart patients, where the accuracy of the proposed method for predicting heart patients is approximately 95%, which has improved in comparison with previous methods.
References
More filters
Journal ArticleDOI
The mobile revolution--using smartphone apps to prevent cardiovascular disease.
TL;DR: This Review assesses the current literature and content of existing apps that target patients with CVD risk factors and that can facilitate behaviour change and evaluates how apps can be used throughout all age groups with different CVD prevention needs.
Journal ArticleDOI
Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection
TL;DR: This paper proposes a Recurrent Neural Networks-based automated cardiac auscultation solution, and explores the use of various RNN models, and demonstrates that these models significantly outperform the best reported results in the literature.
Journal ArticleDOI
Classifying obstructive sleep apnea using smartphones
TL;DR: A reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests and demonstrates the effectiveness of the developed system when compared to the gold standard.
Proceedings ArticleDOI
Validation of heart rate extraction through an iPhone accelerometer
TL;DR: By comparing the extracted heart rate from acquired acceleration data with the extracted one from ECG reference signal, iPhone functioning as the reliable heart rate extractor has demonstrated sufficient accuracy and consistency.
Related Papers (5)
Early and Remote Detection of Possible Heartbeat Problems With Convolutional Neural Networks and Multipart Interactive Training
Krzysztof Wołk,Agnieszka Wołk +1 more