A Novel Approach to Modelling Multi-Channel and Multi-Phase

Signals on an Angular Coordinate Axis (Semani)

Selma Ozaydin

Independent Researcher, Ankara, Turkey

Keywords: Digital Signal Processing, Multi-Channel Signals, Angular Coordinate Axis, Signal Analysis, Pattern Design,

Semani.

Abstract: This study introduces a new digital signal processing method that holistically plots digital signal vectors

collected from multiple channels on an angular graphic axis and aims to obtain a holistic signal matrix in the

specified angular range using a vector interpolation technique. The proposed method, Semani, enables the

visualization, improvement, and analysis of signal parameters based on phase angles and independent

variables using an angular coordinate axis. In the Semani method, multi-channel signals are visualized on a

single graph by plotting them on a coordinate axis encompassing all angular directions. This approach divides

the angular coordinate axis into sections based on its resolution level of angles (segments) and the rate of

change of independent variables (layers) within the analysis window. A graphic pattern (polar, cartesian,

cylinder, sphere, etc.) determined on the angular axis is divided into slices according to segments and layers,

and the signal is plotted in these sections. The proposed signal plotting and analysis model enables holistic

modeling of multi-channel signals collected from different angular directions on a coordinate axis.

Additionally, the vector interpolation method used in this model calculates signal vectors for unknown angular

directions, enriching the signal. This innovative method allows signals collected from multiple channels, such

as EEG, ECG, radar, sonar, and seismic signals, to be effectively visualized on a single graph against their

corresponding independent variables (e.g., time, frequency, distance).

1 INTRODUCTION

A signal is generally defined as a function

representing the mathematical relationship between

at least one independent variable and a dependent

variable that varies over time, carrying information

about the state of a physical change. Signals can be

categorized into two groups: analog signals and

digital signals. Digital signals are composed of data

representing time-dependent variations, derived from

analog measurements converted into digital values,

obtained or generated from various data sources, or

produced via mathematical functions or computer

simulations. The characteristic features of digital

signals, such as amplitude, frequency, wavelength,

and phase, are derived through various mathematical

operations and are used to define a signal's

characteristics. Consequently, digital signals consist

of numerical sets representing signal samples stored

https://orcid.org/0000-0002-4613-9441

in vector sequences, matrix arrays, higher-

dimensional arrays, or other structured data formats.

Multi-phase signals are composed of multiple

sinusoidal components with defined phase angles,

typically sharing the same frequency. These signals

can originate from a single channel or be aggregated

from various channels. Examples include

communication networks, radar, sonar, antenna

systems, and biomedical signals such as EEG, EMG,

and ECG. A major challenge in multi-phase signal

processing systems is designing advanced algorithms

and frameworks that can efficiently handle the

increased complexity, inter-channel interference, and

synchronization issues while meeting the demands

for higher data rates, ultra-low latency, and enhanced

scalability in next-generation networks like 6G. (She

vd., 2021) (Long vd., 2021).

The Semani technique can represent a multi-

channel/multi-phase signal as a single unified signal

on a graph. A literature review highlights a lack of

1028

Ozaydin, S.

A Novel Approach to Modelling Multi-Channel and Multi-Phase Signals on an Angular Coordinate Axis (Semani).

DOI: 10.5220/0013340800003911

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 1, pages 1028-1037

ISBN: 978-989-758-731-3; ISSN: 2184-4305

systems that model digital signal variations

concerning independent variables and phase angles or

visualize sinusoidal components—such as

fundamental frequencies, harmonics, and phase

angles—on a unified graph. Conventional spectral

analysis methods divide data into separate graphs,

complicating the observation of relationships

between frequencies and harmonics. This challenge

intensifies with an increasing number of sinusoidal

components.

Multiple components or phase differences can

exist in multi-channel, multi-phase signals, with each

element typically conveying distinct information.

These signals are encountered in various applications,

including imaging and audio processing, recordings

made with multiple microphones, and biomedical

signals such as brainwaves (EEG) (Kamble &

Sengupta, 2023; Mazlan vd., 2024), cardiac rhythms

(ECG) (Ruan vd., 2022) and myoelectric signals

(EMG) (Rodriguez-Tapia vd., 2020). They are also

relevant in fields like seismic signal analysis (Yin vd.,

2022), telecommunications, and radio frequencies,

where signals are received from multiple antennas or

channels. Despite their importance, the integration of

multi-channel signals remains an unresolved

challenge, and predictions about the signal source

often rely on diverse signal processing techniques.

Among the approaches discussed in the literature,

beamforming is a prominent method used to combine

signals from multiple channels, isolating and

amplifying those originating from a specific source or

direction (Ramírez-Espinosa vd., 2024). This is

achieved by either applying fixed weights to each

channel (linear beamforming) or dynamically

adjusting weights based on channel conditions

(adaptive beamforming). Another widely used

technique is principal component analysis (PCA),

which extracts key components with the highest

variance from multi-channel data, summarizing the

information into a single representative signal (R.

Martin, 2023). Independent component analysis

(ICA) is similarly employed, focusing on separating

independent sources from mixed signals to create a

unified signal form (Melinda vd., 2023). Time-

frequency analysis, including methods such as short-

time Fourier transform (STFT) and wavelet

transform, is used to analyze signals across various

time and frequency domains, facilitating the

combination of these components into a single signal

(Kumar vd., 2021). Additionally, the Hilbert

transform provides valuable insights into the phase

information of a signal by deriving its analytical form,

while neural networks and machine learning

techniques can learn important features from multi-

channel data and integrate them into a single

representation (Engels vd., 2021) (Neupane & Seok,

2020) (Kazemi Lichaee vd., 2024). In

communication systems, signal modulation and

demodulation techniques, such as phase modulation

(PM) or phase shift keying (PSK), play a critical role

in integrating signals transmitted via different

channels by analyzing their phase components.

Despite these advancements, no existing method in

the literature directly models multi-phase signals into

a unified form without intermediate signal analysis,

highlighting the ongoing complexity and challenges

in this field (Pham vd., 2021) (Wei vd., 2023).

Physical changes, represented as signals, are

inherently independent of coordinate systems.

However, mapping them to coordinate axes, as in

current systems, introduces dependencies that distort

their visualization, especially for complex changes.

Semani resolves this by decoupling signals from

coordinate dependencies, allowing flexible graphical

representations. For multi-channel signals, existing

methods analyze vector behaviors for different phase

angles separately, combining results to reconstruct

the original signal. No system predicts intermediate

signal behaviors or unifies these analyses into a single

graphical model, further limiting comprehensive

signal evaluation. Semani addresses these challenges

by providing a novel approach to signal modeling and

analysis. It enables the visualization of multi-

channel/multi-phase signals and their associated

parameters in a unified graphical representation,

significantly simplifying the observation of

relationships between signal components. Moreover,

it uses an interpolation-based technique for

estimating intermediate signal vectors between two

known signal vectors with a specific phase angle,

facilitating more comprehensive signal modeling and

analysis.

The biggest difference between the graphs drawn

on the existing angular coordinate axis definitions and

the proposed method is that in existing methods, one

of the angular axis parameters generally represents

angles and the other represents signal amplitudes. In

the Semani method, one of the axis parameters

represents the angles and the other represents the

independent variables of the signal vectors, and the

signal vectors in each angular direction are

represented over these independent variables.

In the Semani method, when frequency values are

selected as independent variables and signal power

values as amplitude, the energy, frequency, and

vibrations of multi-directional signals —

recommended by the great scientist Nikola Tesla—

can be visualized on a single graph. (Martin, 2022).

A Novel Approach to Modelling Multi-Channel and Multi-Phase Signals on an Angular Coordinate Axis (Semani)

1029

2 MAIN SCOPE OF THE STUDY

The primary aim of this study is to develop a

comprehensive system model capable of analyzing

and modeling any input signal in angular coordinates

across independent variables, such as time and

frequency. To achieve this, input signals are

categorized into two groups: Sinyal1, representing

monophasic signals with uniform behavior across all

angular directions (e.g., time domain audio signals),

and Sinyal2, encompassing multiphasic signals that

exhibit varying characteristics across different

angular directions (e.g., biomedical, seismic, or

antenna radiation signals).

Semani enables the analysis of both Sinyal1 and

Sinyal2 signals by segmenting them into short-time

analysis windows and mapping their behavior

graphically in angular coordinates.

For Sinyal2 signals, the method uses a vector

interpolation method to derive signal vectors for

unmeasured angular directions, creating a signal

matrix that allows comprehensive angular modeling.

The Semani method provides a graphical

representation of signal vectors in different angular

directions and enables the enrichment of the input

signal by deriving signal vectors in unmeasured

directions using the interpolation method. This

approach allows for detailed time-domain analysis or

frequency-domain analysis using standard spectrum

analysis methods (e.g., Fourier Transform) enabling

visualization of key spectral features, such as

fundamental frequencies and harmonics, within an

octave-based frequency band structure.

Semani further introduces the ability to map

signal parameters (e.g., frequency, amplitude, phase

angle) using color palettes, enabling an intuitive

visual representation of numerical values.

Additionally, it supports 2D and 3D modeling in

cylindrical and spherical coordinates, enhancing the

analysis of signal behavior in multidimensional

spaces.

A key application of this system lies in separating

signal and noise components by correlating signal

characteristics with angular coordinates, which is

particularly beneficial for noise analysis in multi-

directional signals like seismic or biomedical data.

This system aims to simplify and enhance the

visualization and interpretation of signal properties

across angular and independent variable domains,

contributing significantly to advancements in signal

analysis and modeling techniques.

2.1 Advantages of the Semani Method

Semani offers significant advancements in signal

modeling and analysis by addressing the limitations

of existing techniques and introducing novel benefits.

It enables comprehensive modeling of digital signals

on graphical axes with layers and segments, capturing

detailed variations in multi-dimensional data.

Decoupling the angular axis from dependent

variables, provides unparalleled flexibility in

designing custom graphical patterns, simplifying the

visualization of complex multi-phase signals in two

or three dimensions. Real-time signal behavior, such

as EEG, EKG, and seismic data, can be monitored

effectively, with dominant resonant frequencies and

harmonics emphasized for deeper analysis.

Semani facilitates the prediction of unmeasured

signal data using adjacent measured values, ensuring

complete signal representation. Outputs can be

converted into formats suitable for machine learning

applications, supporting advanced analytics. It

revolutionizes antenna power radiation diagrams by

enabling realistic modeling of power values across all

directions and distances while allowing independent

analysis of noise and harmonics.

For the first time, multi-phase signals, including

cardiac, brain, seismic, and audio spectrums, can be

directly modeled together in a single graphical

representation. Semani simplifies the modeling of

high-dimensional, complex signals on various

coordinate bases, fostering the development of

innovative visualization tools and analysis

techniques, and making it a groundbreaking approach

for diverse applications.

Semani offers extensive applications across

various industries, enabling advanced modeling of

analog and digital signals. Its capabilities extend to

fields such as medicine, engineering, healthcare,

measurement and evaluation, telecommunications,

and electronics. By facilitating real-time visualization

and multi-dimensional analysis of complex signals—

such as EEG, EKG, EMG, seismic data, radar, sonar,

and audio/music spectra—Semani allows for the

precise and detailed study of these signals.

Key applications include real-time monitoring

and graphical representation of biomedical signals

during patient assessments, enhanced earthquake

simulations for predictive technologies, detailed radar

and sonar analyses for tracking multiple targets, and

comprehensive antenna radiation pattern evaluations.

Semani also addresses challenges in tensor modeling

by offering effective solutions for multi-dimensional

signal modeling, thereby improving efficiency and

accuracy. Its potential spans not only industrial and

BIOSIGNALS 2025 - 18th International Conference on Bio-inspired Systems and Signal Processing

1030

biomedical applications but also groundbreaking

contributions to fields like quantum physics, where it

could provide new insights into particle behavior and

quantum uncertainty principles.

3 METHODOLOGY

Semani enables the modeling and analysis of digital

signals within angular coordinate axes, defined based

on independent variables and phase angles, on a

graphical representation. This approach is

implemented on devices with signal processing

capabilities, allowing both the real-time and post-

recording analysis of signals.

Figure 1: Flowchart of Semani method.

Through windowing techniques, signals are

processed within specific analysis windows,

providing dynamic visualization and evaluation. The

flowchart of the Semani method can be found in

Fig.1. Here, signal vectors (v1, v2, v3,…) were taken

according to their respective phase angles for

modeling. The windowing technique was defined for

short-time analysis. For a graphical display on the

coordinate axis, the change rate of the independent

variable (Kf) and angular resolution (Sf) parameters

were determined. Signal analysis was then started for

successive windows. Within the defined analysis

window, a linear vector interpolation was performed

according to Eq. (1) to calculate the missing signal

vectors (v1, v11, v12, …v2, v21, v22, …v3, v31,

v32,…) for comprehensive visualization purposes

(Fig.2). (Here, the user can prefer another vector

interpolation method, if desired). Signal feature

parameters are derived for graphical visualization.

The format for signal representation—such as

numerical values, colors, symbols, or other visual

elements—is configured based on the graph's design.

The ‘hold-on’ feature was enabled to enable

visualization of all signal data within the analysis

window during the loop process. Then Iterative

processes were initiated for the visualization of signal

data across layers (Kf) and segments (Sf) based on the

calculated boundaries of each segment. Signal data

was displayed on the graph. After visualizing the

windowed data, the ‘hold-on’ feature was disabled

and the cycle continued for successive windows.

Figure 2: Effect of vector interpolation on signal vectors.

Semani provides a robust framework for the real-

time modeling, analysis, and visualization of multi-

A Novel Approach to Modelling Multi-Channel and Multi-Phase Signals on an Angular Coordinate Axis (Semani)

1031

phase signals such as EEG, EKG, seismic data, and

signal spectra. Its layered and angular approach

enables the efficient representation of complex data,

facilitating deeper insights and advanced applications

across industries such as biomedical engineering,

structural analysis, and digital signal processing.

3.1 The Linear Vector Interpolation

Method

Semani enables the modeling of Sinyal2-type multi-

phase signals within angular coordinates by deriving

signal vector samples for intermediate angular

directions not provided as input. Utilizing the

arithmetic averaging method outlined in Equation (1),

signal vectors for these angular segments are

iteratively calculated based on given input signal

vectors. The formula used to calculate the signal

vectors between two input vectors x1 and x2 with an

angle θ is:

𝐵





𝑗

,𝑖







𝑛𝑖



.𝑥





𝑗



 𝑖. 𝑥





𝑗



𝑛

𝑗

 1,..,𝐿,𝑖 1,..,𝑛1

(1)

Here, Bx(j,i) represents the intermediate signal

vectors between x1 and x2, where n is the number of

segments, and L is the length of the signal vectors. By

segmenting the angular coordinate space into equal

intervals and generating a comprehensive signal

matrix, Semani effectively represents Sinyal2's

behavior across the entire angular spectrum. This

approach assumes minimal variation in signal

characteristics across adjacent angular directions.

Additionally, in the Semani method, the signal can be

plotted at the desired resolution by dividing the

angular axis into layers (Kf) based on the rate of

increase of the independent variable. This process

facilitates the modeling of time-domain and

frequency-domain multi-dimensional signal

properties, such as amplitude, power, and phase, with

applications in real-time signal analysis and spectrum

visualization using Fourier Transform, wavelets, or

similar spectral analysis methods. The result is a

robust framework for analyzing Sinyal2 signals

across time and frequency planes.

4 TEST & EVALUATION

The tests were conducted using Sinyal1 and Sinyal2

types of signals, analyzed in both time and frequency

domains. The tests showed that Semani allows for

modeling various signal types, such as audio, EKG,

EEG, and seismic signals, in different angular axes

types. The results from these tests demonstrate

Semani's versatility in signal processing across

various domains, with application examples

including audio, music, and seismic signals.

The provided text details the application of a

signal modeling system using the Semani method for

testing and analyzing both ECG (Electrocardiogram)

and EEG (Electroencephalogram) signals in time and

frequency domains. Here's a summary and

continuation based on the provided information.

4.1 Tests for Sinyal1 (Frequency

Domain)

Tests on audio signals at 44kHz sampling rate

(Fig.3a) showed the system's capability to model

signals in polar coordinates (Fig.3b), offering detailed

frequency spectrum analysis. The results highlighted

how longer analysis windows improve frequency

resolution.

Figure 3a: log-FFT of a speech signal.

Figure 3b: Normalized log-FFT of the speech signal of

Fig3a with Semani method (numbers represent frequencies).

BIOSIGNALS 2025 - 18th International Conference on Bio-inspired Systems and Signal Processing

1032

4.2 Tests for Sinyal2 (Time &

Frequency)

4.2.1 Signal Modeling for ECG Analysis

Semani was used to analyze ECG signals, specifically

from a publicly available 6-channel dataset

containing 10-second long samples recorded at 500

Hz sampling frequency. The dataset includes various

channels (CH1-CH3, AVL, AVR, AVF, V1-V6).

These channels provide both bipolar (CH1-CH3) and

unipolar (AVL, AVR, AVF, V1-V6) derivations.

Figure 4a: A time-domain ECG signal from 6 leads.

During the signal modeling, CH1, CH2, and CH3

were used as input data, with additional derivations

calculated through linear interpolation from these

channels. Fig4a and 4b present a 1s analysis window

outputs of an ECG signal in the time domain. Spectral

analysis was performed using the FFT technique, and

the distribution of the frequency spectrum across

angular phases was displayed as can be seen in Fig 5.

Figure 4b: The time-domain ECG signal of Fig.4a with

Semani method.

Figure 5: An ECG signal in the frequency domain with the

Semani method.

4.2.2 Signal Modeling for EEG Analysis

For EEG signal analysis, Semani used data from

Physionet's database, specifically from a project

involving non-invasive monitoring for epileptic

seizure prediction. The dataset consists of 14 patients,

with each recording lasting 3600 seconds, collected

using the International 10-20 system at a 512Hz

sampling rate.

During the signal modeling, EEG signals from the

electrodes were analyzed in both Cartesian and polar

coordinate systems. The outer region's 10 electrodes

were analyzed (Fp1, Fp2, F8, T4, T6, 02, O1, T5, T3,

A Novel Approach to Modelling Multi-Channel and Multi-Phase Signals on an Angular Coordinate Axis (Semani)

1033

F7), with the results displayed on polar axes,

representing the signal amplitude distributions in

angular directions. The analysis was performed using

a 1-second window, with a sampling rate of 128 Hz,

resulting in 128 layers per second (Fig 6a & 6b).

Fig.7 presents a 3D spherical model of an EEG signal

by using the same data from 10 electrodes in UD

direction. A frame-by-frame analysis of the Semani

method can be seen in Fig.9 and in a frame-by-frame

analysis of the Semani method in an EEG signal can

be seen in Fig.10.

The frequency domain analysis involves

visualizing the spectrum distribution of the EEG

signals across angular phases, with the FFT technique

applied to extract frequency components.

In summary, Semani facilitates advanced signal

modeling, both in time and frequency domains, for

complex biological signals like ECG, EMG, and

EEG. Semani enables detailed analysis of the signals'

amplitude distribution and frequency content, using

polar and 3D coordinate systems for visualization, as

well as spectral analysis through techniques like FFT.

This approach allows for a more comprehensive

understanding of the signals and their characteristics.

Figure 6a: A time-domain EEG signal from 10 electrodes

in a standard 10-20 system.

4.2.3 Signal Modeling for Earthquake

Signals

The Semani method was applied to test and analyze

Sinyal2-type earthquake signals, first discussing their

structure. Earthquake signals are first divided into

Figure 6b: The EEG signal of Fig 6a with Semani method.

Figure 7: An EEG signal in 3D with Semani method.

body waves (P-waves and S-waves) and surface

waves (Rayleigh (R) waves and Love (L) waves).

The data used for analysis, from the AFAD

database, corresponds to a 4.1 magnitude earthquake

on March 29, 2023, in Kahramanmaraş, recorded at a

sampling rate of 100 Hz in three directions: south-

north (SN), east-west (EW), and up-down (UD).

For the time-domain analysis, the signals in EW

(CH1), SN (CH2), and UD (CH3) directions were

used, and linear interpolation generated additional

vectors in angular directions.

The body and surface waves were analyzed

separately due to differences in amplitude and

behavior. The signal data was normalized and

processed using the Semani method, producing a

BIOSIGNALS 2025 - 18th International Conference on Bio-inspired Systems and Signal Processing

1034

matrix according to Eq.1 and representing the signal

amplitudes with color-coded angular directions.

For surface wave analysis, Fig.8a and 8b show

how the signal amplitudes in angular directions are

represented as circular layers, for the 1s analysis

window.

Fig. 8b presents a 3D cylindrical model of surface

waves in the EW, SN, and UD directions, showing

signal amplitudes in circular layers.

Figure 8a: Surface-waves of an earthquake signal in EW,

SN, and UD directions.

Figure 8b: Surface-waves of the earthquake signal of Fig8a

with Semani method (EW, SN & UD directions).

Figure 9: A frame-by-frame analysis of current methods & Semani method.

A Novel Approach to Modelling Multi-Channel and Multi-Phase Signals on an Angular Coordinate Axis (Semani)

1035

Figure 10: Frame-by-frame analysis example of Semani method in an EEG signal.

5 DISCUSSION

Semani introduces a novel framework for signal

modeling and analysis, particularly for complex

biomedical signals such as ECG, EMG, and EEG.

One of the significant advantages of Semani is its

ability to directly represent multi-phase & multi-

channel signals in both 2D and 3D spaces, which

offers a more comprehensive view of signal dynamics

compared to traditional methods. The utilization of

polar and Cartesian coordinate systems for signal

visualization allows for a clearer interpretation of

signal amplitudes across different time intervals and

angular orientations, providing richer insights into the

characteristics of biological signals.

A key innovation of Semani lies in its handling of

multi-dimensional signal data. For ECG signals, the

analysis incorporates both bipolar and unipolar

derivations, providing a more complete

representation of the electrical activity of the heart.

The ability to map these signals onto a 3D coordinate

system allows for a better understanding of spatial

relationships between the various leads and their

contribution to the overall ECG pattern. Similarly, for

EEG signals, Semani's use of the 10-20 international

electrode placement system for signal acquisition and

analysis in both the time and frequency domains

offers a highly accurate and detailed view of brain

activity.

Semani's application in the frequency domain,

particularly through FFT analysis, is also notable. By

dividing signals into layers corresponding to different

frequency bands, it is possible to observe how

specific frequency components evolve over time.

This level of frequency granularity could prove

particularly useful in detecting abnormalities or

subtle changes in both ECG and EEG signals, which

might otherwise go unnoticed with conventional

analysis methods.

Furthermore, Semani's versatility in analyzing

signals from different types of electrodes (such as

CH1-CH3 for ECG and the 10-20 system for EEG)

adds to its potential applicability in diverse clinical

and research settings. While Semani is quite effective

for standard biomedical signal analysis, there is room

for improvements in computational efficiency,

especially when using real-time analysis scenarios.

As the system is expanded to process more complex

signals or higher dimensional data, optimizing

processing speed and scalability will be crucial.

In conclusion, the Semani method provides a

promising framework for signal modeling and

analysis in both time and frequency domains, offering

valuable insights into radar, sonar & biomedical

signals. Future improvements and refinements,

particularly regarding assumptions about electrode

placement and computational efficiency, could

further enhance its utility and applicability in clinical

diagnostics and research.

6 CONCLUSIONS

The Semani method provides a robust and innovative

approach for the modeling and analysis of complex

biological signals, such as ECG and EEG, in both the

time and frequency domains. By utilizing advanced

techniques like linear interpolation, 3D signal

representation, and polar coordinate analysis, Semani

enables detailed visualization of signal amplitude

distributions and frequency spectra. The integration

of FFT for frequency domain analysis further

enhances the system's ability to extract meaningful

insights from the signals. The application of Semani

to both ECG and EEG datasets demonstrates its

BIOSIGNALS 2025 - 18th International Conference on Bio-inspired Systems and Signal Processing

1036

versatility and effectiveness in capturing intricate

signal characteristics, offering a valuable tool for both

clinical and research purposes. The results, as

presented through various graphical representations,

highlight Semani’s potential to improve the precision

and understanding of biomedical signal analysis,

paving the way for enhanced diagnostic tools and

further advancements in the field.

REFERENCES

Engels, F., Hedenrech, P., Wntermantel, M., Stacker, L.,

Al Kad, M., & Zoubr, A. M. (2021). Automotve

Radar Sgnal Processng: Research Drectons and

Practcal Challenges. IEEE Journal of Selected Topcs

n Sgnal Processng, 15(4), 865-878. https://do.org/

10.1109/JSTSP.2021.3063666

Kamble, K., & Sengupta, J. (2023). A comprehensve

survey on emoton recognton based on

electroencephalograph (EEG) sgnals. Multmeda

Tools and Applcatons, 82(18), 27269-27304.

https://do.org/10.1007/s11042-023-14489-9

Kazem Lchaee, F., Salar, A., Jall, J., Bekmohammad

Dalvand, S., Roshanfekr Rad, M., & Mojarad, M.

(2024). Advancements n Artfcal Intellgence for

ECG Sgnal Analyss and Arrhythma Detecton: A

Revew. Internatonal Journal of Cardovascular

Practce,8(2).

https://do.org/10.5812/ntjcardovascpract-143437

Kumar, R., Verma, A. R., Gupta, B., & Kumar, S. (2021).

Dual-Tree Sparse Decomposton of DWT Flters for

ECG Sgnal Compresson and HRV Analyss.

Augmented Human Research, 6(1), 3. https://do.org/

10.1007/s41133-020-00041-z

Long, J., Wang, X., Zhou, W., Zhang, J., Da, D., & Zhu, G.

(2021). A Comprehensve Revew of Sgnal Processng

and Machne Learnng Technologes for UHF PD

Detecton and Dagnoss (I): Preprocessng and

Localzaton Approaches. IEEE Access, 9, 69876-69904.

https://do.org/10.1109/ACCESS.2021.3077483

Mazlan, M. R., Sukor, A. S. A., Adom, A. H., &

Jamaluddn, R. (2024). Revew of analyss of EEG

sgnals for stress detecton. 040007. https://do.org/

10.1063/5.0181590

Melnda, M., Maulsa, O., Nabla, N. H., Yundar, Y., &

Enrko, I. K. A. (2023). Classfcaton of EEG Sgnal

usng Independent Component Analyss and Dscrete

Wavelet Transform based on Lnear Dscrmnant

Analyss. JOIV : Internatonal Journal on Informatcs

Vsualzaton, 7(3), 830-838. https://do.org/10.30630/

jov.7.3.1219

Neupane, D., & Seok, J. (2020). A Revew on Deep

Learnng-Based Approaches for Automatc Sonar

Target Recognton. Electroncs, 9(11), 1972.

https://do.org/10.3390/electroncs9111972

Pham, Q.-V., Nguyen, N. T., Huynh-The, T., Bao Le, L.,

Lee, K., & Hwang, W.-J. (2021). Intellgent Rado

Sgnal Processng: A Survey. IEEE Access, 9, 83818-

83850.

https://do.org/10.1109/ACCESS.2021.3087136

R. Martn. (2023). Robustfed prncpal component

analyss for feature selecton n EEG sgnal

classfcaton. Internatonal Journal of Systematc

Innovaton, 7(5). https://do.org/10.6977/IJoSI.2023

03_7(5).0004

Ramírez-Espnosa, P., Morales-Jmenez, D., & Wong, K.-

K. (2024). A New Spatal Block-Correlaton Model for

Flud Antenna Systems. IEEE Transactons on Wreless

Communcatons, 23(11), 15829-15843. https://do.org/

10.1109/TWC.2024.3434509

Rodrguez-Tapa, B., Soto, I., Martnez, D. M., & Arballo,

N. C. (2020). Myoelectrc Interfaces and Related

Applcatons: Current State of EMG Sgnal Processng–

A Systematc Revew. IEEE Access, 8, 7792-7805.

https://do.org/10.1109/ACCESS.2019.2963881

Ruan, H., Da, X., Chen, S., & Qu, X. (2022). Arrhythma

Classfcaton and Dagnoss Based on ECG Sgnal: A

Mult-Doman Collaboratve Analyss and Decson

Approach. Electroncs, 11(19), 3251. https://do.org/

10.3390/electroncs11193251

She, C., Cheng, P., L, A., & L, Y. (2021). Grand

Challenges n Sgnal Processng for Communcatons.

Fronters n Sgnal Processng, 1, 664331.

https://do.org/10.3389/frsp.2021.664331

We, Z., Qu, H., Jang, W., Han, K., Wu, H., & Feng, Z.

(2023). Iteratve Sgnal Processng for Integrated

Sensng and Communcaton Systems. IEEE

Transactons on Green Communcatons and

Networkng, 7(1), 401-412. https://do.org/10.1109/

TGCN.2023.3234825

Yn, X., Lu, F., Ca, R., Yang, X., Zhang, X., Nng, M., &

Shen, S. (2022). Research on Sesmc Sgnal Analyss

Based on Machne Learnng. Appled Scences, 12(16),

8389. https://do.org/10.3390/app12168389

J. Long, X. Wang, W. Zhou, J. Zhang, D. Da and G. Zhu,

"A Comprehensve Revew of Sgnal Processng and

Machne Learnng Technologes for UHF PD Detecton

and Dagnoss (I): Preprocessng and Localzaton

Approaches," n IEEE Access, vol. 9, pp. 69876-69904,

2021, do: 10.1109/ACCESS.2021.3077483.

Martn, J., “Nkola Tesla: How Energy, Frequences, and

Vbratons Shape Realty”, Amazon Dgtal Servces

LLC - KDP Prnt US, ISBN:9798408952731, 2022

Availability of Data and Materials: The datasets used

and analyzed in the current study are publicly available as

open-source data.

Conflict of Interests: The author declares that the

method presented in this study is subject to a patent

application under the author's name, which may have

potential commercial implications.

A Novel Approach to Modelling Multi-Channel and Multi-Phase Signals on an Angular Coordinate Axis (Semani)

1037