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Low-Overhead Compression of ECG Recordings for Implantable Medical Devices

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National Technical University of Athens   

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Low-Overhead Compression of ECG Recordings for Implantable Medical Devices (EN)
Σωμαράκης, Αντώνιος (EL)
Somarakis, Antonios (EN)
ntua (EL)
Σούντρης, Δημήτριος (EL)
Ματσόπουλος, Γεώργιος (EL)
Πεκμεστζή, Κιαμάλ (EL)
bachelorThesis
2016-06-27
2016-06-27T09:06:07Z
2016-03-24
It is an indisputable fact that Implantable Medical Devices (IMDs) are becoming an integral part of Medical science. IMDs are encountered in a great variety of medical applications. IMDs rely on data acquisition, processing and communication agents in order to sustain and ameliorate the life of the patients. IMDs have limited memory, computational and battery power resources, while collecting, processing and transmitting out information from potentially many sensors. These limitations require that information within the devices be efficiently compressed. Such data compression presents a challenging task, as it must provide high fidelity of the waveform reproduction and high compression ratios on limited size data frames. Also, it must be based on the type of data to be compressed, in order to provide bigger efficiency. In this thesis we try to better up the existing lossy and lossless compression methods. In order to manage that, we use various algorithms and combinations of those in order to find the most efficient scheme. The two main algorithms that we use are LZO encoding algorithm and SPIHT encoding algorithm. We combine these encoding algorithms with various data procession algorithms. Our main attempt is to evaluate the aforementioned algorithms and so we use Electrocardiography (ECG), an extremely widely used biodata which is recorded from IMDs and sent or saved from them. The main evaluation parameters of our thesis are the compression ratio, the Percent Root mean square Difference (PRD) and computational overhead of each algorithm. Finally, based on the evaluation process we conclude that SPIHT with Reordering with fuzzy C means Clustering offer the best compression ratio 25.95 with RPD 4.86 and the best tradeoff between compression ratio and PRD the LZO with Reordering technique with 10.67 compression ratio and 3.13 .As for the lossless algorithms LZO with Reordering with fuzzy C means clustering offers 2.42 compression ratio. (EN)
Εμφυτεύσιμες (EL)
Ηλεκτροκαρδιογράφημα (EL)
Αναλογία συμπίεσης (EL)
Συμπίεση βιοδεδομένων (EL)
Ενεργειακό αποτύπωμα (EL)
LZO (EN)
Percent Root Difference (PRD) (EN)
Compression ratio (EN)
Electrocardiography (ECG) (EN)
Data compression (EN)
Low overhead (EN)
ECG compression ratio (EN)
Implantable Medical Devices (IMDs) (EN)
SPIHT (EN)
English
Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών. Εργαστήριο Μικροϋπολογιστών και Ψηφιακών Συστημάτων VLSI (EL)
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National Technical University of Athens
Digital Library of National Technical University of Athens | Dspace@NTUA
Κεντρική Βιβλιοθήκη Ε.Μ.Π.
Ιδρυματικό Αποθετήριο
Διπλωματικές Εργασίες



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