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.

Multivariate functional clustering for the morphological analysis of electrocardiograph curves / F. Ieva, A.M. Paganoni, D. Pigoli, V. Vitelli. - In: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS. - ISSN 0035-9254. - 62:3(2013 May), pp. 401-418.

Multivariate functional clustering for the morphological analysis of electrocardiograph curves

F. Ieva
Primo
;
2013

Abstract

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.
Electrocardiograph signal ; Functional k-means clustering ; Functional registration ; Wavelets smoothing
Settore SECS-S/01 - Statistica
Settore MED/01 - Statistica Medica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/233363
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