"Nen\u00ED k dispozici"@cs . "Structural, Syntactic, and Statistical Pattern Recognition" . "2004-08-18+02:00"^^ . . "RIV/68407700:21230/04:03099614!RIV/2005/MSM/212305/N" . . . . "Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECGs. These signals normally include several registers and its duration is up to 48 hours. The principal problem for the cardiologists consists of the manual inspection of the whole holter ECG to find all those beats whose morphology differ from the normal synus rhythm. The later analisys of these arrhythmia beats yields a diagnostic from the pacient's heart condition. Using Hidden Markov Models (HMM) for computer clustering has became a very useful tool for cardiologists avoiding the manual inspection. In this paper we improve the performance of the HMM clustering method introducing a preclustering stage in order to diminish the number of elements to be finally processed and reducing the global computational cost. An experimental comparative study is carried out, utilizing records form the MIT-BIH Arrhythmia database. Finally some results ar."@en . "9"^^ . . "Mic\u00F3, P." . "Nov\u00E1k, Daniel" . "RIV/68407700:21230/04:03099614" . "Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECGs. These signals normally include several registers and its duration is up to 48 hours. The principal problem for the cardiologists consists of the manual inspection of the whole holter ECG to find all those beats whose morphology differ from the normal synus rhythm. The later analisys of these arrhythmia beats yields a diagnostic from the pacient's heart condition. Using Hidden Markov Models (HMM) for computer clustering has became a very useful tool for cardiologists avoiding the manual inspection. In this paper we improve the performance of the HMM clustering method introducing a preclustering stage in order to diminish the number of elements to be finally processed and reducing the global computational cost. An experimental comparative study is carried out, utilizing records form the MIT-BIH Arrhythmia database. Finally some results ar." . "Nen\u00ED k dispozici"@cs . "939 ; 947" . "[7CA85F064D77]" . "Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models"@en . "Lisbon" . . "Hidden Markov Models; Holter Electrocardiogram"@en . "Berlin" . "Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models" . . . "1"^^ . . "Nen\u00ED k dispozici"@cs . "3"^^ . "Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models"@en . . "3-540-22570-6" . "Z(MSM 210000012)" . . . . "Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models" . . "Springer-Verlag" . . "581227" . "21230" . "Cuesa, D." .