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EEEN30160: Biomedical Signal Processing Project Classifying PVCs in ECG The Goal of this project is to identify Premature Ventricular Contractions (PVCs) in ECG waveforms. It will involve ECG signal filtering and artifact removal; detection, segmentation and extraction of discriminative features from QRS complexes, and classification of normal vs PVC beats based on those features. Premature ventricular contractions (PVCs) are the most common cause of irregular heart rhythms. Normally, the heartbeat is created by an electrical signal that originates in the sinoatrial (SA) node in the heart’s upper right chamber, the right atrium. The electrical signal moves through theheart. heart From to thethe atrioventricular (AV) node, a cluster of specialized cells in down the centre of the AV node the signal passes along special ‘Purkinje‘ fibres embedded in the heart walls to the ventricles, the lower chambers. When the electrical current arrives in the ventricles, it causes them to contract and pump oxygen-rich blood out to the body. A premature ventricular contraction (PVC) is a ventricular contraction that is mistakenly initiated by the purkinje fibres of the ventricles ahead of the normal initiation by the SA node, which causes an early, inefficient contraction before the atria have managed to fill the ventricles. The SA node rhythm typically persists despite this early contraction, and its signal arrives in on top of the PVC causing a sort of double-contraction of greater force than normal. When a PVC occurs as a single premature beat, patients may describe the feeling as a "palpitation" or "skipped beat." But when PVCs cause these double-contractions it can be strong enough to cause pain or discomfort in the chest. Individuals who have frequent PVCs or a series of them may experience a fluttering sensation in the chest or neck. If PVCs are frequent enough to reduce the heart’s pumping ability, the individual may experience weakness, fainting. Whodizziness is at riskorfor PVCs? Almost everyone has PVCs at some time, from childhood through adulthood. In a study of healthy members of the military, the incidence of PVCs was 0.5% among those under the age of 20 and 2.2% in those over the age of 50. PVCs occur more commonly in older people and in individuals with underlying heart disease, including a history of heart attack. People with a family history of cardiac arrhythmias (abnormal heart rhythm) also have a higher risk for PVCs. Figure 1 shows a snippet of the ECG waveform you will examine in this project, after it has been cleaned up. It is easy to see by eye the compounded Purkinje-then-SA initiated contractions giving rise to a greater and broader QRS deflection. The data file ecgproject.mat contains a noisy ECG recording sampled at 200 Hz with 400 beats in it (‘ecg1’), 93 of which are PVCs (indicated by the vector ‘isPVC’). Your job is to process the signal and extract features which can accurately classify each beat as normal or PVC. Fig. 1. premature ventricular contractions are the 4th and 6th beats Specific Tasks 1. Plot the ECG signal in the time-domain, in units of sec. Try to identify a few PVC beats versus normal beats by zooming in. Is it possible before cleaning up the data? 2. Implement filtering methods to remove high-frequency and low-frequency noise and artifacts in the signal. What kind of artifacts did you find? Are they the kind that require rejection of segments of data, or can they be corrected? If so, how? [hint: sometimes filters produce ‘edge artifacts’ at the beginning and end of the data recording being filtered. A neat trick to reduce these is to extend the data at the ends either by ‘wraparound’ or by reflection, then filtering, then shaving off the extensions] Provide plots that demonstrate how the signal quality has been improved by your signal processing steps. Again tryimplement to identify an a few PVC beats versus normalthe beats 3. Now come up with and algorithm which segments databy– zooming i.e., findsin. each of the individual beats in the overall signal and extracts segments containing them, aligned at some particular point in the single-beat waveform (e.g. P onset, P peak, R peak, Q or S trough, …). You can consult the internet for fancy methods for QRS complex detection, such as the Pan–Tompkins method, but if you use them you must provide a full description of exactly how they work in your own words, and give the source of any code you downloaded. 4. From the segmented beats, select and extract features that you think will potentially discriminate normal from PVC beats. Again you may consult the internet for features but must demonstrate your understanding of them in the report. 5. Implement a classification method to classify each beat as a normal or a PVC. Partition the data into training and test sets – indicate exactly how you have tested/validated and why. How did you select the features? Which turned out to be the best? Compute the accuracy of classification by comparing against the labels provided in isPVC. How many true- positives, false-positive, true-negative, and false-negative were there? Note beats not detected and false detections of beats (if any) by your program. Use graphs to explain your results as necessary. Always label the axes of your graphs and show the proper units.