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dc.contributor.advisorEsmaeily, Amir Sajaden
dc.contributor.authorKishor, Sakshi
dc.date.accessioned2021-04-28T20:48:14Z
dc.date.available2021-04-28T20:48:14Z
dc.date.issued2021
dc.identifier.citationKishor, S. (2021). Anomalies in the classification and detection of heart rate using deep learning. Masters Thesis, Dublin Business School.en
dc.identifier.urihttps://esource.dbs.ie/handle/10788/4257
dc.description.abstractArrhythmias, which in non-medical terms is commonly known as the irregularity in a heartbeat, contributes to approximately 300,000 sudden deaths per year. This incidence rate is higher than the other lethal and dangerous disease like lung cancer, stroke, or a breast cancer. Cardiologists can help patients cope with many types of arrhythmias, but researchers and doctors are kept awake at night by sudden and lethal arrhythmias. This study is an effort to bring down the incidence rate due to heart rate arrhythmias by identifying the irregularities in a heartbeat at early stages and help the cardiologists with their patients. The 5 different categories of heartbeats are classified in this study from publicly available MIT-BIH database which consists of ECG data from various patients. The data has been preprocessed with the help of wavelet transformation tool in MATLAB. To help achieve the classifications of the heartbeats, logistic regression and convolution neural networks algorithms are used. These two models’ evaluations are compared in the form of recall, precision, accuracy, and computational time. A comparative study between the two models is done with the help of tableau. Based on the evaluation, the deep learning model, CNN, gives out the accuracy of 99.09 percent.en
dc.language.isoenen
dc.publisherDublin Business Schoolen
dc.rightsArrhythmias, which in non-medical terms is commonly known as the irregularity in a heartbeat, contributes to approximately 300,000 sudden deaths per year. This incidence rate is higher than the other lethal and dangerous disease like lung cancer, stroke, or a breast cancer. Cardiologists can help patients cope with many types of arrhythmias, but researchers and doctors are kept awake at night by sudden and lethal arrhythmias. This study is an effort to bring down the incidence rate due to heart rate arrhythmias by identifying the irregularities in a heartbeat at early stages and help the cardiologists with their patients. The 5 different categories of heartbeats are classified in this study from publicly available MIT-BIH database which consists of ECG data from various patients. The data has been preprocessed with the help of wavelet transformation tool in MATLAB. To help achieve the classifications of the heartbeats, logistic regression and convolution neural networks algorithms are used. These two models’ evaluations are compared in the form of recall, precision, accuracy, and computational time. A comparative study between the two models is done with the help of tableau. Based on the evaluation, the deep learning model, CNN, gives out the accuracy of 99.09 percent.en
dc.rights.urihttp://esource.dbs.ie/copyrighten
dc.titleAnomalies in the classification and detection of heart rate using deep learningen
dc.typeThesisen
dc.rights.holderCopyright: The publisheren
dc.type.degreenameMSc in Data Analyticsen
dc.type.degreelevelMScen


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