The prediction of cardiac arrhythmia is challenging for professionals in the medical field. It is proposed that presenting a patient's heartbeat data in the form of a musical score can help with the detection of patterns of irregularity, which would aid in the prediction of arrhythmia. It is further proposed that an automated peak detection process could be as accurate as human detection of heartbeat data peaks. When visualising automatically chosen peaks in the form of a musical score, this research aims to show that the detection of heartbeat irregularities and prediction of arrhythmia will be made easier for medical professionals. Through the investigation of ECG wav files in Sonic Visualiser, a software application used to analyze music audio data, this research studied the accuracy of automated MATLAB scripts that calculated and plotted the peaks of heartbeat data. The frequency of errors produced by the programs were calculated, and peak data points were added and/or removed to correct the mistakes produced by the programs in Sonic Visualiser. The results of this research will be used as a tool of reference to optimize and minimize the margin of error of future peak detection programs.
