Technical data sheet: Adaptive Match Filter Method (AMFM) to T-wave alternans detection

 

Language: Matlab, exe

 

Specifications

 

TWA is, by definition, characterized by a specific frequency, that is fTWA. To account for physiological variations of the RR interval, a narrow frequency band, instead of a single frequency, was assumed by Adaptive Match Filter method to characterize the TWA phenomenon. On this basis, the Adaptive Match Filter (AMF) was implemented as a 6th order bidirectional Butterworth pass-band filter having the passing band 2·dfTWA=0.12 Hz wide and centered in fTWA. In particular, the AMF consisted of a cascade of a low pass filter (LPF) with cut-off frequency fLPF= fTWA+ dfTWA, and a high pass filter (HPF) with a cut-off frequency fHPF= fTWA-dfTWA.

 

The inputs of the method are a prefiltered ECG signal and an ‘annotations matrix’ having information about some parameters like R-Peaks sequence, J time-series ( end of QRS complexes ) and Ton , Tmax  e Toff  time-series that are respectively the beginning, the apex and the end of each T-wave. The output is a sinusoidal signal, shown in Fig.1, characterized by a possibly amplitude modulation, with its maxima and minima occurring in correspondence of the T waves. Thus, the TWA amplitude ATWA is obtained as the mean TWA-signal amplitude whereas the minima/maxima location, driven  by the TWA-signal phase as shown in Fig.2, provides the TWA location or delay DTWA (measured with respect to the previous R peak).

 

fig1_AMFM

       Fig.1. Simulated ECG with TWA (filter input) and corresponding TWA signal (filter output).

 

 

 fig2_AMFM

Fig. 2. ECG signal with overlapping the TWA signal (dotted) with different phases : “Early TWA” (localized along the ST segment and the first half of the T-wave), “Central TWA” (occurring around the T-wave apex) and “Late TWA” (involving the last part of the T-wave).

 

 

 

Block diagram

 

 schemaablocchi2_AMFM

 

 

Features

 

Advantages:

 

+ capable to provide a correct detection of time-varying TWA-amplitude signals;

+ preprocessing stage not required;

+ not significantly affected by all interferences (residual noise, baseline wandering and respiration modulation ) due to the fact that  the heart-rate adaptive match filter, which receives the ECG tracing as input  yields the suppression of all ECG and interferences frequency components;

+ it is not affected by the presence of misalignments because it does not rely on T-wave windowing.

 

 

References 

 

1. L Burattini, W Zareba, R Burattini. Automatic detection of microvolt T-wave alternans in Holter recordings: Effect of baseline wandering. Biomedical Signal Processing And Control 2006, 1(2): 162-168.

 

2. L Burattini, W Zareba, R Burattini. Adaptive match filter based method for time vs. amplitude characterization of microvolt ECG T-wave alternans. Annals of Biomedical Engineering 2008, 36:1558-1564.

 

3. L Burattini, W Zareba, R Burattini. Assessment of physiological amplitude, duration and magnitude of ECG T-wave alternans. Annals of Noninvasive Electrocardiology 2009, 14:366-374.

 

4. L Burattini, S Bini, R Burattini. Comparative analysis of methods for automatic detection and quantification of microvolt T-wave alternans.Medical Engineering & Physics 2009, 31:1290-1298.

 

5. L Burattini, W Zareba, R Burattini. Identification of gender-related normality regions for T-wave alternans. Annals of Noninvasive Electrocardiology 2010; 15:328-336.

 

6. L Burattini, W Zareba, R Burattini. Response to Dr. Selvaraj’s Comments on the “Assessment of Physiological Amplitude, Duration and Magnitude of ECG T-Wave Alternans.” Ann Noninvasive Electrocardiol. 2009;14:366–374. Annals of Noninvasive Electrocardiology 2010, 15:185-186.

 

7. L Burattini, S Bini, R Burattini. Automatic microvolt T-wave alternans identification in relation to ECG interferences surviving preprocessing.Medical Engineering & Physics 2011; 17-30.

 

8. SC Man, PV De Winter, AC Maan, JThijssen,  JW Borleffs, WP van Meerwijk, M Bootsma, L van Erven, EE van der Wall, MJ Schalij, L Burattini, R Burattini, CA Swenne. Predictive Power of T-wave Alternans and of Ventricular Gradient 2 Hysteresis for the Occurrence of Ventricular Arrhythmias in Primary Prevention ICD Patients. Journal of Electrocardioliology 2011.

 

9. L Burattini, R Burattini. Response to the Letter to the Editor entitled “Temporal locations of repolarization alternans within the electrocardiogram JT-interval in patients with acute myocardial infarction and healthy subjects” Medical Engineering & Physics 2012;34:395.

 

10. L Burattini, W Zareba, R Burattini. Is T-wave alternans T-wave amplitude dependent? Biomedical Signal Processing And Control 2012; 7:358-364.

 

11. L Burattini, S Bini, R Burattini. Repolarization alternans heterogeneity in healthy subjects and acute myocardial infarction patients. Medical Engineering & Physics 2012; 34:305-312.

 

12. L Burattini, S Man, R Burattini, CA Swenne. Comparison of standard vs. orthogonal ECG leads for T-wave alternans identification. Annals of Noninvasive Electrocardiology 2012; 17:130-140.

 

13. L Burattini, S Man, R Burattini, CA Swenne. Response to Dr. Madias’ comments on “T-wave alternans by a 16-lead Electrocardiogram System”. Annals of Noninvasive Electrocardiology 2013;   18(1):100-101.

 

14. L Burattini, S Man, CA Swenne. T-wave alternans dependency on T-wave amplitude in exercise electrocardiographic recordings. International Journal of Bioelectromagnetism  2013; 15(1):90-96.

 

15. L Burattini, S Man, CA Swenne. The power of exercise-induced T-wave alternans to predict ventricular arrhythmias in patients with implanted cardiac defibrillator. Journal of Healthcare Engineering 2013; 4(2):167-184.  

 

16. S Bini, L Burattini. Quantitative Characterization of Repolarization Alternans in Terms of Amplitude and Location: What Information from Different Methods? Biomedical Signal Processing And Control, 2013; 8:675-681.