Low-Overhead Compression of ECG Recordings for Implantable
Medical Devices

Σωμαράκης, Αντώνιος; Somarakis, Antonios

dc.contributor.author	Σωμαράκης, Αντώνιος	el
dc.contributor.author	Somarakis, Antonios	en
dc.date.accessioned	2016-06-27T09:06:07Z
dc.date.issued	2016-06-27
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/42853
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.12716
dc.rights	Default License
dc.subject	Εμφυτεύσιμες	el
dc.subject	Ηλεκτροκαρδιογράφημα	el
dc.subject	Συμπίεση βιοδεδομένων	el
dc.subject	Ενεργειακό αποτύπωμα	el
dc.subject	Αναλογία συμπίεσης	el
dc.subject	Implantable Medical Devices (IMDs)	en
dc.subject	Electrocardiography (ECG)	en
dc.subject	Data compression	en
dc.subject	ECG compression ratio	en
dc.subject	Low overhead	en
dc.subject	Percent Root Difference (PRD)	en
dc.subject	Compression ratio	en
dc.subject	SPIHT	en
dc.subject	LZO	en
dc.title	Low-Overhead Compression of ECG Recordings for Implantable Medical Devices	en
heal.type	bachelorThesis
heal.classification	Βιοϊατρική μηχανική	el
heal.classificationURI	http://data.seab.gr/concepts/e21d30fc29c7c38310b1b9c23590a18fd030f6f2
heal.dateAvailable	2017-06-26T21:00:00Z
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2016-03-24
heal.abstract	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
heal.advisorName	Σούντρης, Δημήτριος	el
heal.committeeMemberName	Πεκμεστζή, Κιαμάλ	el
heal.committeeMemberName	Ματσόπουλος, Γεώργιος	el
heal.committeeMemberName	Σούντρης, Δημήτριος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών. Εργαστήριο Μικροϋπολογιστών και Ψηφιακών Συστημάτων VLSI	el
heal.academicPublisherID	ntua
heal.numberOfPages	53 σ.	el
heal.fullTextAvailability	true