Multivariate Functional Clustering for the Morphological Analysis of Electrocardiograph Curves

Summary Cardiovascular ischaemic diseases are one of the main causes of death all over the world. In this class of pathologies, a quick diagnosis is essential for a good prognosis in reperfusive treatment. In particular, an automatic classification procedure based on statistical analysis of teletransmitted electrocardiograph (‘ECG’) traces would be very helpful for an early diagnosis. This work presents an analysis of ECG traces, either physiological or pathological, of patients whose 12-lead prehospital ECG has been sent to the 118 Dispatch Center in Milan by life-support personnel. The statistical analysis starts with a preprocessing step, where functional data are reconstructed from noisy observations and biological variability is removed by a non-linear registration procedure. Then, a multivariate functional k-means clustering procedure is carried out on reconstructed and registered ECGs and their first derivatives. Hence, a new semi-automatic diagnostic procedure, based solely on the ECG morphology, is proposed to classify ECG traces; finally, the performance of this classification method is evaluated.