53 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
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
Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis
A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases. Two input data to the classify are evaluated: TDA features, and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms (98.68% to VF; 99.05% to VT; 98.76% to normal sinus; and 99.09% to Other rhythms) with specificity values higher than 97.16% in all cases. In addition, a higher accuracy of 99.51% was obtained when discriminating between shockable (VT/VF) and non-shockable rhythms (99.03% sensitivity and 99.67% specificity). These results show that the use of TDA-derived geometric features, combined in this case this the k-Nearest Neighbor (kNN) classifier, raises the classification performance above results in previous works. Considering that these results have been achieved without preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapie
Comprehensive electrocardiographic diagnosis based on deep learning
Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified on manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals
Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions
Abstract Background Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the most serious cardiac arrhythmias that require quick and accurate detection to save lives. Automated external defibrillators (AEDs) have been developed to recognize these severe cardiac arrhythmias using complex algorithms inside it and determine if an electric shock should in fact be delivered to reset the cardiac rhythm and restore spontaneous circulation. Improving AED safety and efficacy by devising new algorithms which can more accurately distinguish shockable from non-shockable rhythms is a requirement of the present-day because of their uses in public places. Method In this paper, we propose a sequential detection algorithm to separate these severe cardiac pathologies from other arrhythmias based on the mean absolute value of the signal, certain low-order intrinsic mode functions (IMFs) of the Empirical Mode Decomposition (EMD) analysis of the signal and a heart rate determination technique. First, we propose a direct waveform quantification based approach to separate VT plus VF from other arrhythmias. The quantification of the electrocardiographic waveforms is made by calculating the mean absolute value of the signal, called the mean signal strength. Then we use the IMFs, which have higher degree of similarity with the VF in comparison to VT, to separate VF from VTVF signals. At the last stage, a simple rate determination technique is used to calculate the heart rate of VT signals and the amplitude of the VF signals is measured to separate the coarse VF from VF. After these three stages of sequential detection procedure, we recognize the two components of shockable rhythms separately. Results The efficacy of the proposed algorithm has been verified and compared with other existing algorithms, e.g., HILB 1, PSR 2, SPEC 3, TCI 4, Count 5, using the MIT-BIH Arrhythmia Database, Creighton University Ventricular Tachyarrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database. Four quality parameters (e.g., sensitivity, specificity, positive predictivity, and accuracy) were calculated to ascertain the quality of the proposed and other comparing algorithms. Comparative results have been presented on the identification of VTVF, VF and shockable rhythms (VF + VT above 180 bpm). Conclusions The results show significantly improved performance of the proposed EMD-based novel method as compared to other reported techniques in detecting the life threatening cardiac arrhythmias from a set of large databases.</p
Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia
Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.This study was supported by the Ministerio de EconomΓa, Industria y Competitividad, Gobierno de EspaΓ±a (ES) (TEC-2015-64678-R) to UI and EA and by Euskal Herriko Unibertsitatea (ES) (GIU17/031) to UI and EA. The funders, Tecnalia Research and Innovation and Banco Bilbao Vizcaya Argentaria (BBVA), provided support in the form of salaries for authors AP, AA, FAA, CF, EG, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section
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
Use of Wavelets in Electrocardiogram Research: a Literature Review
Currently the introduction and detection of heart abnormalities using electrocardiogram (ECG) is very much. ECG conducted many research approaches in various methods, one of which is wavelet. This article aims to explain the trends of ECG research using wavelet approach in the last ten years. We reviewed journals with the keyword title "ecg wavelet" and published from 2011 to 2020. Articles classified by the most frequently discussed topics include: datasets, case studies, pre-processing, feature extraction and classification/identification methods. The increase in the number of ECG-related articles in recent years is still growing in new ways and methods. This study is very interesting because only a few researchers focus on researching about it. Several approaches from many researchers are used to obtain the best results, both by using machine learning and deep learning. This article will provide further explanation of the most widely used algorithms against ECG research with wavelet approaches. At the end of this article it is also shown that the critical aspect of ECG research can be done in the future is the use of datasets, as well as the extraction of characteristics and classifications by looking at the level of accuracy
Deep learning for healthcare applications based on physiological signals: A review
Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi
- β¦