Collins, Leslie MLaPorte, Emma2019-06-072019-06-072019https://hdl.handle.net/10161/18934<p>The main function of the human heart is to act as a pump, facilitating the delivery of oxygenated blood to the many cells within the body. Heart failure (HF) is the medical condition in which a heart cannot adequately pump blood to the body, often resulting from other conditions such as coronary artery disease, previous heart attacks, high blood pressure, diabetes, or abnormal heart valves. HF afflicts approximately 6.5 million adults in the US alone [1] and manifests itself often in the form of fatigue, shortness of breath, increased heart rate, confusion, and more, resulting in a lower quality of life for those afflicted. At the earliest stage of HF, an adequate treatment plan could be relatively manageable, including healthy lifestyle changes such as eating better and exercising more. However, the symptoms (and the heart) worsen overtime if left untreated, requiring more extreme treatment such as surgical intervention and/or a heart transplant [2]. Given the magnitude of this condition, there is potential for large impact both in (1) automating (and thus expediting) the diagnosis of HF and (2) in improving HF treatment options and care. These topics are explored in this work. </p><p>An early diagnosis of HF is beneficial because HF left untreated will result in an increasingly severe condition, requiring more extreme treatment and care. Typically, HF is first diagnosed by a physician during auscultation, which is the act of listening to sounds from the heart through a stethoscope [3]. Therefore, physicians are trained to listen to heart sounds and identify them as normal or abnormal. Heart sounds are the acoustic result of the internal pumping mechanism of the heart. Therefore, when the heart is functioning normally, there is a resulting acoustic spectrum representing normal heart sounds, that a physician listens to and identifies as normal. However, when the heart is functioning abnormally, there is a resulting acoustic spectrum that differs from normal heart sounds, that a physician listens to and identifies as abnormal [3]–[5]. </p><p>One goal of this work is to automate the auscultation process by developing a machine learning algorithm to identify heart sounds as normal or abnormal. An algorithm is developed for this work that extracts features from a digital stethoscope recording and classifies the recording as normal or abnormal. An extensive feature extraction and selection analysis is performed, ultimately resulting in a classification algorithm with an accuracy score of 0.85. This accuracy score is comparable to current high-performing heart sound classification algorithms [6]. </p><p>The purpose of the first portion of this work is to automate the HF diagnosis process, allowing for more frequent diagnoses and at an earlier stage of HF. For an individual already diagnosed with HF, there is potential to improve current treatment and care. Specifically, if the HF is extreme, an individual may require a surgically implanted medical device called a Left Ventricular Assist Device (LVAD). The purpose of an LVAD is to assist the heart in pumping blood when the heart cannot adequately do so on its own. Although life-saving, LVADs have a high complication rate. These complications are difficult to identify prior to a catastrophic outcome. Therefore, there is a need to monitor LVAD patients to identify these complications. Current methods of monitoring individuals and their LVADs are invasive or require an in-person hospital visit. Acoustical monitoring has the potential to non-invasively remotely monitor LVAD patients to identify abnormalities at an earlier stage. However, this is made difficult because the LVAD pump noise obscures the acoustic spectrum of the native heart sounds. </p><p>The second portion of this work focuses on this specific case of HF, in which an individual’s treatment plan includes an LVAD. A signal processing pipeline is proposed to extract the heart sounds in the presence of the LVAD pump noise. The pipeline includes down sampling, filtering, and a heart sound segmentation algorithm to identify states of the cardiac cycle: S1, S2, systole, and diastole. These states are validated using two individuals’ digital stethoscope recordings by comparing the labeled states to the characteristics expected of heart sounds. Both subjects’ labeled states closely paralleled the expectations of heart sounds, validating the signal processing pipeline developed for this work. </p><p>This exploratory analysis can be furthered with the ongoing data collection process. With enough data, the goal is to extract clinically relevant information from the underlying heart sounds to assess cardiac function and identify LVAD disfunction prior to a catastrophic outcome. Ultimately, this non-invasive, remote model will allow for earlier diagnosis of LVAD complications.</p><p>In total, this work serves two main purposes: the first is developing a machine learning algorithm that automates the HF diagnosis process; the second is extracting heart sounds in the presence of LVAD noise. Both of these topics further the goal of earlier diagnosis and therefore better outcomes for those afflicted with HF.</p>Electrical engineeringHeart failureHeart soundsLVADsMachine learningSignal processingMaster's thesis