4 research outputs found
ΠΠ±Π·ΠΎΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ Π½Π΅ΠΎΠ±Ρ ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ
Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal.Β Β Lipchak D. A., Chupov A. A. Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review. Ural Radio Engineering Journal. 2021;5(4):380β409. (In Russ.) DOI: 10.15826/ urej.2021.5.4.004.Β Π€ΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΡ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ² ΡΠ΅ΡΠ΄ΡΠ° ΡΡΠΈΡΠ°Π΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°ΡΡΠ΅ΠΉΡΡ ΠΏΡΠΈΡΠΈΠ½ΠΎΠΉ Π²Π½Π΅Π·Π°ΠΏΠ½ΠΎΠΉ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ΅ΡΠ΄ΡΠ°. Π’Π°ΠΊΠ°Ρ ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΡ ΠΈ ΡΠ°ΡΡΠΎ ΠΏΡΠ΅Π΄ΡΠ΅ΡΡΠ²ΡΡΡΠ°Ρ Π΅ΠΉ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ²Π°Ρ ΡΠ°Ρ
ΠΈΠΊΠ°ΡΠ΄ΠΈΡ β ΡΡΠΎ ΡΠΈΡΠΌΡ ΡΠ΅ΡΠ΄ΡΠ°, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ ΡΠ΅Π°Π³ΠΈΡΠΎΠ²Π°ΡΡ Π½Π° ΡΠΊΡΡΡΠ΅Π½Π½ΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠΎΠΊΠΎΠ²ΡΡ ΡΠ΅ΡΠ°ΠΏΠΈΡ ΠΈ Π²Π΅ΡΠ½ΡΡΡΡΡ ΠΊ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΠΎΠΌΡ ΡΠΈΠ½ΡΡΠΎΠ²ΠΎΠΌΡ ΡΠΈΡΠΌΡ ΠΏΡΠΈ ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΏΠΎΡΠ»Π΅ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ΅ΡΠ΄ΡΠ° Ρ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΌ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎΠΉ Π½Π°ΡΠΎΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΠ΅ΡΠ΄ΡΠ°. ΠΠ΄Π½Π°ΠΊΠΎ ΡΡΡΠ½Π°Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠ° ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΠΠ Π½Π° Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΏΠ°ΡΡΠ΅ΡΠ½Π° ΡΠ°ΠΊΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ, ΡΡΠ΅Π±ΡΡΡΠ΅ΠΉ Π½Π΅ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π² ΡΡΡΠ΅ΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ, ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅Π²ΡΠΏΠΎΠ»Π½ΠΈΠΌΠΎΠΉ Π² ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠ³ΠΎ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°. ΠΠΎΡΡΠΎΠΌΡ Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΎΡΡΡΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠΈΠ»ΠΈ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π°ΡΠΈΡΠΌΠΈΠΉ Ρ ΡΡΠ½ΠΊΡΠΈΠ΅ΠΉ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΡΠ΅ΡΠ°ΠΏΠΈΠΈ Ρ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ Π²ΡΡΠΎΠΊΠΎΠ²ΠΎΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΠΈΠΌΠΏΡΠ»ΡΡΠ°, Π²ΡΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΠΎ Π΄Π»Ρ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°, ΡΠ½ΠΈΠΌΠ°Π΅ΠΌΠΎΠ³ΠΎ Ρ ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ΠΎΠ² Π½Π°ΡΡΠΆΠ½ΠΎΠ³ΠΎ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠ»ΠΈ ΠΏΠΎΠ»ΡΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΎΡΠ°, Ρ ΡΠ΅Π»ΡΡ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΎΠΊΠ°Π·Π°Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΠ΅ Π²ΠΎ Π²ΡΡΡΠΎΠ΅Π½Π½ΠΎΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΌ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΠΏΠΎΠ»ΡΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²Π½Π΅ΡΠ½ΠΈΡ
Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΎΡΠΎΠ². Π Π°Π±ΠΎΡΠ° Π²ΠΊΠ»ΡΡΠ°Π΅Ρ ΠΎΠ±Π·ΠΎΡ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ, ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΡΠΆΠ°ΡΠΈΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ ΡΠΈΠ³Π½Π°Π»Π° ΠΠΠ.Β Β ΠΠΈΠΏΡΠ°ΠΊ Π. Π., Π§ΡΠΏΠΎΠ² Π. Π. ΠΠ±Π·ΠΎΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ. Ural Radio Engineering Journal. 2021;5(4):380β409. DOI: 10.15826/urej.2021.5.4.004.
Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review
Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. Ventricular fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that can respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals for the presence of a pattern of such arrhythmias is a risky and time- consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, for the automatic diagnosis of such conditions, systems for the computer classification of arrhythmias to decide on the need for electric cardioversion with the parameters of a high-voltage pulse, calculated adaptively for each patient, are widely used. This paper discusses methods for analyzing the electrocardiographic signal taken from external automatic or semi-automatic defibrillator electrodes to decide the need for defibrillation, which is applicable in the embedded software of automatic, semi-automatic external defibrillators. The paper includes an overview of applicable filtering techniques and subsequent algorithms for extracting, classifying, and compressing features for the ECG signal. Both advantages and disadvantages are discussed for the studied algorithms. Β© 2022 IEEE.Russian Foundation for Basic Research, Π Π€Π€Π, (20-37-90037)The reported study is funded by RFBR according to research project No. 20-37-90037
Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review
ΠΠΎΡΡΡΠΏΠΈΠ»Π°: 19.12.2021. ΠΡΠΈΠ½ΡΡΠ° Π² ΠΏΠ΅ΡΠ°ΡΡ: 18.01.2022.Received: 19.12.2021. Accepted: 18.01.2022.Π€ΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΡ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ² ΡΠ΅ΡΠ΄ΡΠ° ΡΡΠΈΡΠ°Π΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°ΡΡΠ΅ΠΉΡΡ ΠΏΡΠΈΡΠΈΠ½ΠΎΠΉ Π²Π½Π΅Π·Π°ΠΏΠ½ΠΎΠΉ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ΅ΡΠ΄ΡΠ°. Π’Π°ΠΊΠ°Ρ ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΡ ΠΈ ΡΠ°ΡΡΠΎ ΠΏΡΠ΅Π΄ΡΠ΅ΡΡΠ²ΡΡΡΠ°Ρ Π΅ΠΉ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ²Π°Ρ ΡΠ°Ρ
ΠΈΠΊΠ°ΡΠ΄ΠΈΡ β ΡΡΠΎ ΡΠΈΡΠΌΡ ΡΠ΅ΡΠ΄ΡΠ°, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ ΡΠ΅Π°Π³ΠΈΡΠΎΠ²Π°ΡΡ Π½Π° ΡΠΊΡΡΡΠ΅Π½Π½ΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠΎΠΊΠΎΠ²ΡΡ ΡΠ΅ΡΠ°ΠΏΠΈΡ ΠΈ Π²Π΅ΡΠ½ΡΡΡΡΡ ΠΊ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΠΎΠΌΡ ΡΠΈΠ½ΡΡΠΎΠ²ΠΎΠΌΡ ΡΠΈΡΠΌΡ ΠΏΡΠΈ ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΏΠΎΡΠ»Π΅ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ΅ΡΠ΄ΡΠ° Ρ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΌ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎΠΉ Π½Π°ΡΠΎΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΠ΅ΡΠ΄ΡΠ°. ΠΠ΄Π½Π°ΠΊΠΎ ΡΡΡΠ½Π°Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠ° ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΠΠ Π½Π° Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΏΠ°ΡΡΠ΅ΡΠ½Π° ΡΠ°ΠΊΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ, ΡΡΠ΅Π±ΡΡΡΠ΅ΠΉ Π½Π΅ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π² ΡΡΡΠ΅ΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ, ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅Π²ΡΠΏΠΎΠ»Π½ΠΈΠΌΠΎΠΉ Π² ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠ³ΠΎ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°. ΠΠΎΡΡΠΎΠΌΡ Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΎΡΡΡΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠΈΠ»ΠΈ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π°ΡΠΈΡΠΌΠΈΠΉ Ρ ΡΡΠ½ΠΊΡΠΈΠ΅ΠΉ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΡΠ΅ΡΠ°ΠΏΠΈΠΈ Ρ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ Π²ΡΡΠΎΠΊΠΎΠ²ΠΎΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΠΈΠΌΠΏΡΠ»ΡΡΠ°, Π²ΡΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΠΎ Π΄Π»Ρ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°, ΡΠ½ΠΈΠΌΠ°Π΅ΠΌΠΎΠ³ΠΎ Ρ ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ΠΎΠ² Π½Π°ΡΡΠΆΠ½ΠΎΠ³ΠΎ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠ»ΠΈ ΠΏΠΎΠ»ΡΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΎΡΠ°, Ρ ΡΠ΅Π»ΡΡ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΎΠΊΠ°Π·Π°Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΠ΅ Π²ΠΎ Π²ΡΡΡΠΎΠ΅Π½Π½ΠΎΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΌ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΠΏΠΎΠ»ΡΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²Π½Π΅ΡΠ½ΠΈΡ
Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΎΡΠΎΠ². Π Π°Π±ΠΎΡΠ° Π²ΠΊΠ»ΡΡΠ°Π΅Ρ ΠΎΠ±Π·ΠΎΡ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ, ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΡΠΆΠ°ΡΠΈΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ ΡΠΈΠ³Π½Π°Π»Π° ΠΠΠ.Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal
Seinale prozesaketan eta ikasketa automatikoan oinarritutako ekarpenak bihotz-erritmoen analisirako bihotz-biriketako berpiztean
Tesis inglΓ©s 218 p. -- Tesis euskera 220 p.Out-of-hospital cardiac arrest (OHCA ) is characterized by the sudden loss of the cardiac function, andcauses around 10% of the total mortality in developed countries. Survival from OHCA depends largelyon two factors: early defibrillation and early cardiopulmonary resuscitation (CPR). The electrical shock isdelivered using a shock advice algorithm (SAA) implemented in defibrillators. Unfortunately, CPR mustbe stopped for a reliable SAA analysis because chest compressions introduce artefacts in the ECG. Theseinterruptions in CPR have an adverse effect on OHCA survival. Since the early 1990s, many efforts havebeen made to reliably analyze the rhythm during CPR. Strategies have mainly focused on adaptive filtersto suppress the CPR artefact followed by SAAs of commercial defibrillators. However, these solutionsdid not meet the American Heart AssociationΒΏs (AHA) accuracy requirements for shock/no-shockdecisions. A recent approach, which replaces the commercial SAA by machine learning classifiers, hasdemonstrated that a reliable rhythm analysis during CPR is possible. However, defibrillation is not theonly treatment needed during OHCA, and depending on the clinical context a finer rhythm classificationis needed. Indeed, an optimal OHCA scenario would allow the classification of the five cardiac arrestrhythm types that may be present during resuscitation. Unfortunately, multiclass classifiers that allow areliable rhythm analysis during CPR have not yet been demonstrated. On all of these studies artefactsoriginate from manual compressions delivered by rescuers. Mechanical compression devices, such as theLUCAS or the AutoPulse, are increasingly used in resuscitation. Thus, a reliable rhythm analysis duringmechanical CPR is becoming critical. Unfortunately, no AHA compliant algorithms have yet beendemonstrated during mechanical CPR. The focus of this thesis work is to provide new or improvedsolutions for rhythm analysis during CPR, including shock/no-shock decision during manual andmechanical CPR and multiclass classification during manual CPR