JECTIVE: The paper presents a diagnostic algorithm for classifying cardiac tachyarrhythmias for implantable cardioverter defibrillators (ICDs). The main aim was to develop an algorithm that could reduce the rate of occurrence of inappropriate therapies, which are often observed in existing ICDs. To achieve low energy consumption, which is a critical factor for implantable medical devices, very low computational complexity of the algorithm was crucial. The study describes and validates such an algorithm and estimates its clinical value.METHODOLOGY: The algorithm was based on the heart rate variability (HRV) analysis. The input data for our algorithm were: RR-interval (I), as extracted from raw intracardiac electrogram (EGM), and in addition two other features of HRV called here onset (ONS) and instability (INST). 6 diagnostic categories were considered: ventricular fibrillation (VF), ventricular tachycardia (VT), sinus tachycardia (ST), detection artifacts and irregularities (including extrasystoles) (DAI), atrial tachyarrhythmias (ATF) and no tachycardia (i.e. normal sinus rhythm) (NT). The initial set of fuzzy rules based on the distributions of I, ONS and INST in the 6 categories was optimized by means of a software tool for automatic rule assessment using simulated annealing. A training data set with 74 EGM recordings was used during optimization, and the algorithm was validated with a validation data set with 58 EGM recordings. Real life recordings stored in defibrillator memories were used. Additionally the algorithm was tested on 2 sets of recordings from the PhysioBank databases: MIT-BIH Arrhythmia Database and MIT-BIH Supraventricular Arrhythmia Database. A custom CMOS integrated circuit implementing the diagnostic algorithm was designed in order to estimate the power consumption. A dedicated Web site, which provides public online access to the algorithm, has been created and is available for testing it.RESULTS: The total number of events in our training and validation sets was 132. In total 57 shocks and 28 antitachycardia pacing (ATP) therapies were delivered by ICDs. 25 out of 57 shocks were unjustified: 7 for ST, 12 for DAI, 6 for ATF. Our fuzzy rule-based diagnostic algorithm correctly recognized all episodes of VF and VT, except for one case where VT was recognized as VF. In four cases short lasting, spontaneously ending VT episodes were not detected (in these cases no therapy was needed and they were not detected by ICDs either). In other words, a fuzzy logic algorithm driven ICD would deliver one unjustified shock and deliver correct therapies in all other cases. In the tests, no adjustments of our algorithm to individual patients were needed. The sensitivity and specificity calculated from the results were 100% and 98%, respectively. In 126 ECG recordings from PhysioBank (about 30min each) our algorithm incorrectly detected 4 episodes of VT, which should rather be classified as fast supraventricular tachycardias. The estimated power consumption of the dedicated integrated circuit implementing the algorithm was below 120nW.CONCLUSION: The paper presents a fuzzy logic-based control algorithm for ICD. Its main advantages are: simplicity and ability to decrease the rate of occurrence of inappropriate therapies. The algorithm can work in real time (i.e. update the diagnosis after every RR-interval) with very limited computational resources.

jectiv paper present diagnost algorithm classifi cardiac tachyarrhythmia implant cardiovert defibril icd main aim develop algorithm reduc rate occurr inappropri therapi often observ exist icd achiev low energi consumpt critic factor implant medic devic low comput complex algorithm crucial studi describ valid algorithm estim clinic valuemethodolog algorithm base heart rate variabl hrv analysi input data algorithm rrinterv extract raw intracardiac electrogram egm addit two featur hrv call onset on instabl inst diagnost categori consid ventricular fibril vf ventricular tachycardia vt sinus tachycardia st detect artifact irregular includ extrasystol dai atrial tachyarrhythmia atf tachycardia ie normal sinus rhythm nt initi set fuzzi rule base distribut on inst categori optim mean softwar tool automat rule assess use simul anneal train data set egm record use optim algorithm valid valid data set egm record real life record store defibril memori use addit algorithm test set record physiobank databas mitbih arrhythmia databas mitbih supraventricular arrhythmia databas custom cmos integr circuit implement diagnost algorithm design order estim power consumpt dedic web site provid public onlin access algorithm creat avail test itresult total number event train valid set total shock antitachycardia pace atp therapi deliv icd shock unjustifi st dai atf fuzzi rulebas diagnost algorithm correct recogn episod vf vt except one case vt recogn vf four case short last spontan end vt episod detect case therapi need detect icd either word fuzzi logic algorithm driven icd deliv one unjustifi shock deliv correct therapi case test adjust algorithm individu patient need sensit specif calcul result respect ecg record physiobank min algorithm incorrect detect episod vt rather classifi fast supraventricular tachycardia estim power consumpt dedic integr circuit implement algorithm nwconclus paper present fuzzi logicbas control algorithm icd main advantag simplic abil decreas rate occurr inappropri therapi algorithm can work real time ie updat diagnosi everi rrinterv limit comput resourc