**Summary**

Development of an analytical method that accurately measures various indexes (PQRST
cycle
and size) necessary for ECG (Electrocardiogram) diagnosis and processes them to
quantified
values.

**Classify data in commonly used methods**

** Noise elimination with filtering technique such as Highpass
Filtering**

Actual ECG signals contain a lot of noise, which interfere with the accurate
detection of Q R S peaks, etc.

Noise elimination by designing and applying appropriate filters step
by step.

- Example of Filter Design

■
Filters for P T Detection / Center Frequency=10 Hz / Band width=12 Hz

■
Filter for R Detection / Center Frequency=36 Hz / Band width=34 Hz

** Calculate ECG diagnosis elements into digitized data by converting ECG signals
into
Hilbert to obtain Nyquist diagram and applying Circle Fitting.**

Many studies have been done to develop algorithms for ECG peak detection, but show
limitations on unstable signals.

Identify the ECG diagnosis elements accurately using the Hilbert
transformation
method, and solve the problem by deriving it using digitized data methods.(related part
is
applied for PCT patent)

- Examples of Traditional algorithm research

** Detection Algorithm for ECG using Double Difference and RR Interval
Processing**

d1(i) = e(i+1) - e(i), i = 1,2... n-1

d2(j) = d1(j+1) - d1(j), j = 1,2... n-2

d(j) = `[d2(j)] ^{2}`

- Test results for some diseased patients

LEADS | Ⅰ | Ⅱ | Ⅲ | aVR | aVL | aVF | V1 | V2 | V3 | V4 | V5 | V6 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Total R | 507 | 507 | 507 | 507 | 507 | 507 | 507 | 507 | 507 | 507 | 507 | 507 |

Detected R | 507 | 505 | 506 | 506 | 504 | 506 | 506 | 505 | 506 | 506 | 506 | 506 |

TP | 507 | 505 | 506 | 506 | 504 | 506 | 506 | 506 | 506 | 506 | 506 | 506 |

FN | 0 | 2 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 2 | 1 |

Sensitivity(%) | 100 | 99.6 | 99.8 | 99.8 | 99.4 | 99.8 | 99.8 | 99.6 | 99.8 | 99.8 | 99.8 | 99.8 |

The overall detection sensitivity is
99.8%.
Detection of false peak is almost negligoble. The algorithm shows good performance
for
data affected with
low frequency noises like base-line wander.

ref. :
https://www.sciencedirect.com

- R Peak Detection by existing method (example of a normal signal)

-> For normal signals, R peak detection shows excellent results.

-> Here, the peak is detected for values greater than 0.2.

-> It shows inappropriate results for unstable signals. Applying a 0.2 (threshold)
value, as
shown above, will result in missing peak and lowering the threshold hold value will
result
in too many peaks being detected.

■
ECG Graph

■
Complete R peak detection results using improved filters and Hilbert
transformations

two-time differential for peak detection is generally employed, and the study above
suggests
that detection sensitivity is 99.8%, but only for normal signals. The two-time
differential
method has limitations in detecting the correct peak for unstable
signals.

- Hilbert Conversion of ECG Signals

-> If you convert an ECG signal to Hilbert, you can obtain an imaginary part of the
signal.
The pink graph is the imaginary part of the ECG.

- Nyquist Diagram

-> If Imaginary part data obtained by ECG data and Hilbert transformation is drawn in
X-Y
Graph format, it is as above. This is called the Nyquist diagram.

- Circle Fitting and Calculating Numerical Data

-> Circle fitting the P R T peak from the Nyquist diagram and obtain the diameter of
each
part.

-> The diameter of the circle corresponds to the amplitude of P R T.

- Hilbert Transformation, Nyquist Diagram and 3D ECG

■
(Rear) : An imaginary graph obtained by Hilbert conversion

■
(floor) : raw ECG graph

■
(left-hand side) : Nyquist Black: 3D ECG Graph

*** The data used here utilized the ECG database of 'Physikalisch-Technische Bundesanstalt(PTB), the National Metrology Institute of Germany'.

When a certain real signal is x(t) and the Hilbert-transformed signal is xˆ(t), the signal xThe signum function of f is defined as follows:

The Fourier transform of the Hilbert transform of x(t) is defined as follows

F{(xˆ)}=−j sgn f⋅F{x(t)}

Now, it is possible to have Nyquist plot if the real part of xp(t) is the y-axis and the imaginary part of x

Typical ECG Signal is shown above.

The blue line was obtained using Hilbert Transform. Let's do this as an Imagine part. The pink line is drawn by obtaining the envelope using (Figure 1) Real part and (Figure 2) Imaginary part.

Figure 3. is a graph drawn to help you understand. This is often referred to as the Nyquist Plot, with ECG Signal's Real part on the Y-axis and Imaginary part on the X-axis.

There are several methods to obtain the axis deviation, but here, as in the figure above, the sum of vectors is obtained and expressed as the slope of the vector sum. The angle took the positive direction of the Y-axis to 0 degrees.

First, the Nyquist diagram for the 12 lead ECG is as follows.

By the method of Figure 4,5, eight data of healthy controls, myocardial infarction, and bundle branch block, respectively, are shown in Figures 6, 7, and 8 as follows.

-> Table 1 and 2 show the summary of above three cases.

In the case of Bundle branch blocks, left and right asymmetries occur frequently. This is shown in Table 3 compared to Healthy controls.

-> Figures 11 to 13 show V1 to V6 in a summary.