Syntactic recognition of ECG signals by attributed finite automata

Koski, Antti; Juhola, Martti; Meriste, Merik

doi:10.1016/0031-3203(95)00052-6

Cited by 88 publications

(48 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We decide on PLA for the following reasons. Piecewise linear approximations are already widely used and accepted in the medical and financial domains [16,21]. There are many useful distance measures defined on PLA, including weighed measures [20], time warping [27], Markov model based measures [12] and lower bounding approximations to the Euclidean distance.…”

Section: Time Series Approximationmentioning

confidence: 99%

See 1 more Smart Citation

Online amnesic approximation of streaming time series

Palpanas

Vlachos

Keogh

et al.

Proceedings. 20th International Conference on Data Engineering

103

View full text Add to dashboard Cite

show abstract

Section: Time Series Approximationmentioning

confidence: 99%

“…Many applications in diverse domains, produce voluminous amounts of time series [29,26]. Examples of such applications exist in finance [29], medicine [21], meteorology, oceanography [26], manufacturing, network management [17], sensor networks [11], and other domains.…”

Section: Introductionmentioning

confidence: 99%

Online amnesic approximation of streaming time series

Palpanas

Vlachos

Keogh

et al.

Proceedings. 20th International Conference on Data Engineering

103

View full text Add to dashboard Cite

show abstract

“…Heartbeat features have been extracted from multiple domains: temporal (Koski et al, 1995;Molina et al, 2007), Fourier (Berger et al, 1986), and discrete cosine transform (Plataniotis et al, 2006). Within a domain, features are extracted as either raw (Israel et al, 2005), texture (Porta et al, 2001), power spectrum (Barros and Ohnishi, 2001;Stridh et al, 2004), and PCA/ ICA (Garcia et al, 1998;Barros et al, 2000).…”

Section: Feature Extractionmentioning

confidence: 99%

Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

View full text Add to dashboard Cite

This paper reviews the emerging research into exploitation of heartbeat data as a biometric for human identification. A variety of methods have been proposed for acquiring heartbeat signatures and a range of processing methods has been examined. We approach the biometric identification and verification problem by characterising the three major factors affecting performance: individual variants, environmental variants, and sensor variants. The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors. Within each component, we have identified the relevant research. Where possible, we have tied these research papers to practical examples using high resolution ECG data. The research indicates that the heartbeat contains rich information about the individual, their level of anxiety, and the cardiac state.Keywords: biometrics; heartbeat; ECG; sensing system; cardiovascular function; human identification. areas of interest include novel biometrics, signal and image processing, quantification and modelling of data quality, and cognitive modeling. He was a Principal Investigator for DARPA's Human Identification at a Distance Program (HumanID). He serves on planning committees for IEEE and SPIE, and has served on several advisory panels for the Department of Defence and the Department of Energy. He has authored over a hundred journal and conference papers and holds a PhD in Mathematical Statistics from Yale University.

show abstract

“…Trahanias and Skordalakis (1990) and Koski, Juhola and Meriste (1995) developed pattern recognition methods for ECG signals based on an attributed grammar. Attributed grammars are convenient ways to analyze numerical strings because they combine syntactic and statistical pattern recognition.…”

Section: Attribute Grammars and Medical Signalsmentioning

confidence: 99%

Classification of time series patterns from complex dynamic systems

Schryver¹,

Rao²

1998

View full text Add to dashboard Cite

Syntactic recognition of ECG signals by attributed finite automata

Cited by 88 publications

References 18 publications

Online amnesic approximation of streaming time series

Online amnesic approximation of streaming time series

Heartbeat biometrics: a sensing system perspective

Classification of time series patterns from complex dynamic systems

Contact Info

Product

Resources

About