Lezione 3 Voltmetri Diagnostica strumentale in cardiologia ECG Applicazioni scientifiche della misura in medicina

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1 Corsi di Laurea in Tecniche Di Fisiopatologia Cardiocircolatoria E Perfusione Cardiovascolare Dr. Andrea Malizia 1 Lezione 3 Voltmetri Diagnostica strumentale in cardiologia ECG Applicazioni scientifiche della misura in medicina

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33 A LabVIEW based real-time software to automatically recognize off-normal ECG values Dr. Riccardo Rossi Dr. Jean-François Ciparisse Dr. Gianluca Gentile Dr. Andrea Malizia

34 Aim of the Work Development of software able to recognise pathologies through ECG signal The software is developed in LabVIEW programming language The authors describe how the pathologies are detected and discuss the results The ECG signals and the clinical data are taken from an online database [1,2]

35 ECG signal Electrocardiography (ECG) is a medical test used to detect myocardial pathologies ECG records the electrical activity of the hearth by means of electrodes located in different body position ECG is compounded by 12 signal: Limb leads: I, II and III Augmented limb leads: avr, avl and avf Precordial leads: v1, v2, v3, v4, v5 and v6

36 ECG signal The majority of pathologies can be detected analysing the characteristics of P wave, QRS complex, T wave and their relative distances ST interval PQ segment ST segment PQ interval

37 Software approach The high variability of ECG signals make difficult the development of software through a deterministic approach. Consequently, we realise the software using a tolerance approach We follow the guidelines in [3] in order to detect each pathologies In this work we discuss 5 detections: Hearth rate Atrial fibrillation Old inferior myocardial infarction Old lateral myocardial infarction Old septal myocardial infarction

38 Software Functioning: Wave detection Low pass filter and bias removing function is applied to the raw signal

39 Software Functioning: Wave detection QRS complex are identified through a maximum and minimum detection function in the precordial leads. R: Max > threshold Vi Q = Min < threshold Vi & t min i < t(r i ) S = Min < threshold Vi & t min i > t(r i ) P wave and T wave are then recognized lowering the maximum threshold P wave and T wave are searched in I and II P = Max > threshold Vi & t max i < t(r i ) T = Max > threshold Vi & t max i > t(r i )

40 Hearth Rate Hearth rate detection is performed making the average of QRS complex distances HR bpm = 60 t = 60 t R i t(r i 1 ) t t

41 Detection of myocardial pathologies: Atrial Fibrillation Atrial fibrillation is the irregular heart rate of the patient The software computes the pathology comparing each QRS distance with the heart rate reciprocal 2,5s 2 s 2,8s 2 s

42 Detection of myocardial pathologies: Old inferior myocardial infarction Old (or former) inferior myocardial infarction is detected if pathologic Q waves appear in two leads between II, III and avf Pathologic Q = if V Q i > 1 3 V R i II III avf Normal II III avf Old Inferior Infarction

43 Detection of myocardial pathologies: Old septal myocardial infarction Old (or former) septal myocardial infarction is detected if: R wave is absent in V1 and V2 V R i V2 < tolerance; V R i V1 < tolerance R wave in V2 is smaller than in V1 V R i V2 < V R i V1 V1 V2 Normal V1 V2 Old Septal Infarction V1 V2 Old Septal Infarction

44 Detection of myocardial pathologies: Old lateral myocardial infarction Old (or former) lateral myocardial infarction is detected if: There is a pathologic Q wave in V5 and V6 Pathologic Q = if V Q i > 1 3 V R i V5 V6 Normal V5 V6 Old Inferior Infarction

45 Software Output The software make a statistics of the detection of each pathologies: Pathology percentage: Number of detection Number of QRS complexes A medical examination is necessary if the pathology percentage exceeds a threshold.

46 Results Software detections are compared with clinical data Accuracy = Number of detection confirmed by clinical data Patient analysed

47 Conclusions The software is able to detect the pathologies with an acceptable accuracy. Anyway, some pathologies may involve the detection of other pathologies (false positive). Consequently, the software can be used as a tool by doctors and CANNOT replace them. Many other pathologies will be inserted into the software in order to have a more complete tool. References 1. A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Imanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng and H. E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals, Circulation, vol. 101, no. 23, pp. e215-e220, PhysioNet, [Online]. Available: 3. R. Russo and G. Fadini, L'interpretazione dell'elettrocardiogramma - Manuale di rapido apprendimento, Piccin, 2004.

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88 88 RIFERIMENTI 1. Elementi di Fisica Biomedica. Edises, E. Scannicchio E. Giroletti 2. Massimiliano Morena. Accuratezza, precisione, tipi di errori e cifre significative dei dati analitici». IIS Gobetti - Marchesini Casale sezione Tecnica Chimica e Materiali Analisi chimica, elaborazione dati e Laboratorio LIBRO DI TESTO Lezione 2