ECG monitoring. A software tool for deriving time and frequency parameters

Marcel STANCIU, Mihaela ALBU, Anatolie BOEV University Politehnica of Bucharest (UPB),

Bucharest, ROMANIA albu@ieee.org

Abstract — In this paper is presented a software application which performs dynamic analysis of ECG tachogram using time framing (data windows) and appropriate sliding steps, as chosen by investigator. The application provides a high degree of versatility and the paper presents the algorithms, some experimental results and their significance.

Keywords - dynamic analysis, ECG tachogram, time and frequency domain analysis, heart rate variability.

I. INTRODUCTION The autonomic nervous system (ANS) is that part of the

nervous system that controls the body’s visceral functions, including heart, gastrointestinal tract and different glands secretion among many other vital activities. It is well known that mental and emotional states directly affect the ANS.

Recent studies [1,2] have examined the influence of emotions on the ANS utilizing the analysis of Heart Rate Variability (HRV), which serves as a dynamic investigation window into the autonomic function and balance. This quantity, derived from electrocardiogram (ECG) data, is a measure of the naturally occurring beat to beat changes in the heart rate.

HRV is a non-invasive analysis method for the study of consecutive heartbeats variations. A schematic diagram of a system dedicated to the acquisition, recording and processing of the ECG signals [3] is presented in Fig. 1.

Figure 1. ECG data acquisition, recording and processing system From an ECG record, each QRS complex is detected,

determined as the time intervals between the „R” peaks from sinus node origin. Further, the ECG signal processing tools involve ECG recording, artifacts rejection, extracting normal RR series (NN) and concludes with the computation of the HRV parameters: NNmean, SDNN, RMSSD, TP, VLFnorm, LFnorm, HFnorm, and LF/HF (parameters of the HRV analysis).

The most relevant parameters of the HRV analysis – performed both in time and in frequency domains – are the following:

• NNmean [ms] – mean of all normal RR(NN) intervals ; • SDNN [ms] – standard deviation of all NN intervals ; • RMSSD [ms] – the square root of the mean of the sum of

the squares of differences between adjacent NN intervals ; • TP [ms2] – total power for the analyzed NN intervals

(0.003÷0.4 Hz); • VLF [ms2] – signal power in the very low frequency

range (0.003÷0.04Hz); • LF [ms2] – signal power in the low frequency range

(0.04÷0.15 Hz); • HF [ms2] – signal power in the high frequency range

(0.15÷0.4 Hz);

• VLFn [%] – VLF power in normalized units:

[ ]%100⋅=TP

VLFVLFn

• LFn [%] – LF power in normalized units:

[ ]%100⋅=TPLFLFn

• HFn[%] – HF power in normalized units;

[ ]%100⋅=TPHFHFn

• LF/HF [-] – ratio LF/HF (an index of the sympato - vagal balance).

II. DYNAMIC ANALYSIS OF HEART RATE VARIABILITY

The application designed and implemented in Matlab environment has several parts [4, 5]. As a result of the preprocessing phase, following the signal acquisition, the ECG tachogram (Fig. 2) is derived. A dedicated module performs the HRV analysis in frequency domain, by applying algorithms of spectral estimation to the ECG tachogram. Fig. 3 shows the ECG spectrogram corresponding to the signal in Fig. 2.

This work supported by the national grants CEEX – Viasan nr. 146, 2006-2008:” Multimarker Evaluation of Cardiac Resynchronization Therapy in Chronic Heart Failure Patients” and PNCDI2 - nr. 42108, 2008-2011: “Periprocedural Markers of Catheter Ablation Success in Patients with Paroxystic/Persistent Atrial Fibrillation”.

ECG recording and amplifier

Data acquisition

system

PC: data analysis processing

RR data editing

HRV analysis: time & frequency domain

NN data sequence

Artifacts identification and rejection

MeMeA 2009 - International Workshop on Medical Measurements and Applications Cetraro, Italy May 29-30, 2009

978-1-4244-3599-9/09/$25.00 ©2009 IEEE 116

Figure 2. ECG tachogram as registered from a healthy subject

Figure 3. ECG spectrogram using data in Fig. 1

(green – VLF; red – LF; blue - HF)

Dynamic analysis of HRV uses settable length of the NN vectors, with sliding windows chosen by the user. Performing such an analysis needs to consider following steps: 1. the NN intervals data are loaded and the algorithm

resulting in the tachogram is performed ; 2. the user selects the analysis window size, NP, from a set of

predefined lengths (64÷1024 points); 3. the user selects the degree of superposition of the adjacent

windows (the sliding windows rate), DF, from a set of predefined values, (10÷ 100% from NP);

4. the program displays the number of resulting windows (NF) and, for each window, the matrix having as elements the computed HRV parameters in time domain (NNmean, SDNN and RMSSD) and in the frequency domain (TP, VLFnorm, LFnorm, HFnorm and LF/HF ratio) is presented (with the option to export in Excel).

5. for an enhanced analysis the program displays also the HRV parameters evolution – a so-called dynamic analysis - (NNmean, SDNN, RMSSD, TP, VLFn, LFn, HFn and LF/HF): Fig. 4 – Fig.7.

As an example, the flow diagram of the software tool we developed for an ECG recording/monitoring of 30 minutes is described below:

• load the “17” NN file; • the program displays the number of points in data file

(number of NN intervals): NRR =2235; • it is selected the number of points for analysis:

NP=256; • it is selected the sliding windows rate (% from NP):

DF=25%; • the program displays the number of windows, NF=31

with sliding rate, A=64 points (NN intervals); • the program computes and displays the matrices of

HRV parameters in time domain (NNmean, SDNN and RMSSD) - tables 1, and frequency domain (TP, VLFn, LFn, HFn and LF/HF ratio) - table 2. In the first column of the tables are presented the average time of the window lengths (T [min]);

TABLE 1.

The evolution of HRV parameters in time domain (example).

T

[min]

NNmed

[ms]

SDNN

[ms]

RMSSD

[ms]

2.0076

2.9429

3.8818

.....

906.37

904

887.84

.....

60.302

56.969

44.726

.....

44.138

42.025

36.708

.....

Average

windows

(NNmed) avg

[ms]

(SDNN)avg

[ms]

(RMSSD) avg

[ms]

— 841.96 57.881 33.69

TABLE 2.

The evolution of HRV parameters in frequency domain (example).

T

[min]

TP

[ms2]

VLFn

[%]

LFn

[%]

HFn

[%]

LF/HF

[ - ]

2.0076

2.9429

3.8818

.....

551.7

531.3

401.06

.....

11.374

13.437

14.047

.....

34.366

31.368

27.786

.....

54.254

55.193

58.166

.....

0.63342

0.56833

0.4777

.....

Average

windows

(TP) mean

[ms2] (VLFn) avg

[%] (LFn) avg

[%] (HFn) avg

[%] (LF/H)avg

[ - ]

— 570.2 30.224 35.917 33.855 3.1821

• the program displays the HRV parameters evolution in

time (Fig. 4 – Fig.7); • the application performs a statistical analysis of data;

Fig. 8 and Fig. 9 show the histograms of some HRV parameters and some representative correlations for the chosen parameters.

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Figure 4. Evolution of HRV parameters (example):

NP=128; DF=10% (sliding windows)

Figure 5. Evolution of HRV parameters (example):

NP=256; DF=25% (sliding windows) / OPTIMUM CHOICE

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Figure 6. Evolution of HRV parameters (example): NP=512; DF=10% (successive windows)

Figure 7. Evolution of HRV parameters (example):

NP=512 (≈ 5-8 min.); DF=100% (successive windows)

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The principal purpose is to display diagrams of HRV parameters evolution in time and frequency domain derived from the tachogram using dynamic analysis technique. Our 30 minutes continuous ECG tachogram in Fig. 2 is from a subject in three successive postures: clinostatism (zone I), orthostatism (zone II) and sitting (zone III) (10 minutes for each position) with the two transitions from one position in another. This approach enables us to have an adequate recording length for analysis, but with visible differences of the NN means for each position.

It is presented an analysis of the optimal selection of the windows dimension (i.e., the number of NN intervals - NP) and of the window rate moving/sliding – DF. Figures 4÷7 show comparative results to other recorded data – for different settings for NP and DF. For a higher resolution in determining the HRV parameters smaller dimensions of the windows (NP) and of the moving/sliding windows rate (DF) are necessary.

Initially, small windows dimension and window rate moving/sliding are used with a progressive increase of theses values. The least window length chosen was of 128 points, because a smaller window length (64 and or 32 points) supposes compromising the analysis in frequency domain and frequency parameters of HRV are affected.

The windows dimension of 128 points and the 10% window rate moving/sliding was taken as a reference point and was compared with parameters evolution drift in other sites of window length and moving/sliding windows. A too short sliding window might result in a too high number of windows and therefore computational difficulties without an important informational advantage. The same trend of HRV parameters is being remarked until 256 points of windows dimension and 25% window rate moving/sliding.

The trend of HRV parameters for NP≥512 and DF>10% is severe modified (Fig. 6 and Fig. 7). For NP=512 and DF=100% settings, the information about short time physiological states are lost. Unfortunately, the many current medical equipments for HRV analysis uses usually 512 or 1024 points of windows dimension and 100% window rate moving/sliding for frequency parameters.

From the above mentioned data and our experience, the results corresponding to the selection used in Fig. 5 (NP=256 and DF=25%) ensures an optimal result.

The medical remark that can be made for our HRV analysis, especially for optimal values of NP and DF (NP=256, DF=25%), are the following:

• RMSSD and SDNN value are significant increase in transit states of high level of stress;

• in less stress states (in clinostatism/zone I from tachogram and sitting/zone III from tachogram) frequency HRV parameters values, LFn and LF/HFn, are reduced and stable while HFn values are high. In more large stress state (orthostatism – positional stress/ zone II from tachogram) the values are inverted (high values for LFn and LF/HFn and low values for HFn) with important fluctuations.

Statistics analysis and histogram display of HRV parameters is another step of our program. Fig. 8 shows the HRV parameters histograms (for concordant windows dimensions, NP). As we can see parameters value distribution is apparently unsystematic. This fact is explained by 3 stationary states and 2 transitions from one state to other.

Figure 8. Histograms of some HRV parameters (NP=256; DF=25%) derived from the ECG registration in Fig. 2)

The visualization of each HRV parameters correlation

diagrams by the physician in accessible format, the final step of HVR analysis, complement the ensemble image of the investigator, and enable better phenomenon understanding. Fig. 9 displays representative correlation diagrams of HRV parameters in time and frequency domain (for concordant windows dimensions, NP). For our subject investigated (Fig. 2 tachogram), we can observe there are a good correlation between NNmean and HFn, SDNN and TP, RMSSD and HFn.

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Figure 9. Correlation diagrams (NP=256; DF=25%) for ECG data shown in Fig. 1.

Finally, we can said the program offer the possibility to involve a physician in all phases of the HRV analysis, starting with the ECG recording, relevant sections’ selection for analysis, parameter choice until the final results are achieved. These results confer important information about HRV parameters evolution (sympato - vagal balance state) in different physiological state and even in the transitory states.

III. CONCLUSION Applying the algorithm to various ECG recordings, following

remarks can be derived:

• an improper choice of the NP and DF parameters might lead to false conclusions (from a medical interpretation): such results might contribute to wrong assessments regarding the patient’ actual state; most of the very recent software on the market, processing data from ECG recorder systems, are performing a HRV analysis on a 10 minutes window (NP≈512) and with 100% sliding (succesive windows); finally, in order to attain a higher rate of success when characterizing the

time evolution of physiological stages, it is required that the windows length is as small as possible (with a tradeoff of the computational time) and the dimension of the sliding window has to be adequately chosen;

• the method of dynamic analysis performed to the ECG signals can be considered a handy instrument for the medical staff during the patient monitoring phase. Moreover, it can be easily adapted for deriving other clinic parameters when both their evolution in time and the frequency domain features are of importance.

ACKNOWLEDGMENT The authors wish to express their gratitude for the valuable

comments of dr. rer. nat. Ralf Neurohr. We also want to thank the team at the Emergency Hospital in Bucharest for providing the excellent conditions for performing the experimental analysis.

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[2] Marek Malik, John Camm A. (ed.), Dynamic Electrocardiography, 2004. [3] Heart rate variability, Standards of measurement, physiological

interpretation, and clinical use, European Heart Journal, 17, 354–381 1996.

[4] Albu Mihaela, Stanciu M., Boev A., Multiresolution Analysis of ECG Signals. Application to the Poly-Spectrum12 Evaluation, Proc. of the 16th CSCS Conference, Bucharest, 2007.

[5] Stanciu, M., Boev, A., Albu Mihaela, Pantelimon Brânduşa, Dynamic Monitoring System of Heart Varibility Parameters, Proc. of the 5th International Conference on Electrical and Power Engineering, EPE-2008, Iasi, 3÷5 October, 2008.

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