ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-70
Часть–6_ июня –2025
224
2181-3187
IMPROVING THE EFFICIENCY OF NUMERICAL PROCESSING
ALGORITHMS FOR BIOMEDICAL SIGNALS IN ARTIFICIAL
INTELLIGENCE
TATU named after Muhammad al-Khwarazmi, assistant of the Department of
computer systems
Norboyeva Mahliyo Rustamovna
mahliyonorboyeva15@gmail.com
+998 99 155 74 95
TATU named after Muhammad al-Khwarazmi, leading specialist in technology
transfer, incubation and acceleration department
Naimov Akhadjon Tojimirza o‘g‘li
naimovahadjon@gmail.com
+998 33 355 15 05
Abstract:
Artificial intelligence technologies make it possible to radically
increase the effectiveness of digital processing of biomedical signals in the field of
modern medicine. Biomedicine signals are complex and variable data that reflect the
various physiological processes of the human div, and their precision analysis is
important in further improving diagnostic, monitoring and treatment processes in
medicine. Traditional digital signal processing methods often face limitations in the
complete and efficient processing of these complex signals. Therefore, advanced
artificial intelligence algorithms open up new opportunities in this area, which serves
to increase the efficiency of algorithms for digital processing of biomedical signals.
Key words:
artificial intelligence, medicine, digital technology, algorithms,
biomedicine, models, capabilities, transformation.
Biomedicine signaling is characterized by its complexity and variability. Signals
such as heart rate, brain waves, electrical muscle activity change dynamically over time
and are often contaminated with noise, artifacts, and other impurities. In this
complexity and noisy environment, traditional digital signal processing methods such
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-70
Часть–6_ июня –2025
225
2181-3187
as filters, wave packets, and Fourier transforms are used in signal purification and basic
feature separation. However, these techniques often have limitations in detecting all
fine patterns in the signal, failing to provide full efficiency in analyzing complex and
inaccurate signals. Artificial intelligence algorithms, specifically machine learning and
deep learning technologies, provide new approaches to digital processing of
biomedical signals. Machine learning algorithms allow automation of feature
extraction from a signal and optimization of classification processes. These algorithms
learn from large amounts of data and respond with high accuracy to new data. For
example, machine learning methods are effectively used to identify different heart
rhythm conditions or to monitor pathological changes in brain waves.[1]
In-depth learning models, on the other hand, have great potential in direct analysis
of Biomedicine signals, automatic feature separation, and time-dependent pattern
recognition. Architectures such as convolutional neural networks and recurrent neural
networks allow for a high degree of precision analysis by studying complex structures
of signals. These techniques are successfully used in the analysis of cardiac
electrocardiograms, brain electroencephalograms and many other signals. Another
important aspect of digital processing of biomedical signals using artificial intelligence
is the possibility of real-time operation. In a clinical setting, it is important to obtain a
quick and accurate result, and artificial intelligence algorithms are an effective tool in
meeting this requirement. Real-time detection of heart rhythm disturbances or
monitoring changes in brain activity is of great importance in providing emergency
medical care. Also, with the help of artificial intelligence algorithms, the possibility of
personalized medical services is expanding, taking into account the physiological
characteristics of individual patients.[2]
Several key factors are important to improve the effectiveness of artificial
intelligence in digital performance of biomedical signals. The availability of a quality
and diverse database is required first of all. Artificial intelligence algorithms learn from
a large number of data and contain different states, giving more reliable results. Also,
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-70
Часть–6_ июня –2025
226
2181-3187
the fact that the model is understandable and consistent facilitates its acceptance in a
clinical setting. It is important for medical professionals to be clear how the model is
making decisions and what it is based on. In addition, ensuring the confidentiality and
security of information is of great importance in the medical field, and special attention
should be paid to this aspect in artificial intelligence systems. There are also some
problems with the applications of artificial intelligence in digital performance of
biomedical signals. The lack of quality information, the high need for information, as
well as the limited ability of the model to generalize, are cited as major problems. In
the clinical setting, extensive testing and certification are required to ensure the
reliability and safety of artificial intelligence systems. At the same time, it is important
to work in cooperation with human specialists and take into account their experience,
since artificial intelligence systems are not able to fully establish human decisions, but
support it.[3]
In the future, the role of artificial intelligence in digital processing of biomedical
signals is expected to increase further. With the help of new algorithms, improved
neural networks and integrated systems, artificial intelligence makes revolutionary
changes in the field of Biomedicine. With these technologies, the possibility of constant
monitoring of the health of patients, early detection of diseases and the development
of personalized treatment plans will expand. At the same time, the impact of artificial
intelligence systems on human life and their ethical aspects should also not be
overlooked. The role of artificial intelligence technologies in improving the efficiency
of algorithms for digital processing of biomedical signals is important not only in
technical achievements, but also in improving the quality and efficiency of the medical
field. With these technologies, diagnostic processes in medicine accelerate, the
possibilities of early detection and monitoring of diseases expand, which serves to
improve the quality of life of patients. At the same time, the cooperation of artificial
intelligence with human specialists, the support of their knowledge and experience,
ensures more effective results in the field of Medicine.[4]
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-70
Часть–6_ июня –2025
227
2181-3187
Conclusion:
In conclusion, artificial intelligence is an important tool in improving the
efficiency of numerical processing algorithms for Biomedical signals. It provides new
opportunities for in-depth analysis of complex and noisy signals, automation, real-time
operation, and personalized medical services. At the same time, it is necessary to focus
on such issues as quality data, model intelligibility and data security. The role of
artificial intelligence in the digital operation of biomedical signals will further expand
in the future and serve to bring about revolutionary changes in the medical field. This
is important in improving human health and making medical care more efficient.
References:
1. Khudoyberganov A., Tursunov S. Digital processing of artificial intelligence
and biomedical signals. Journal of Medicine and Information Technology, 2024.
2. Mirzaev D., Karimova N. Artificial intelligence approaches in biomedical
signal processing. Uzbek medical scientific journal, 2023.
3. Rustamov B., Islamova M. The role of artificial intelligence in improving the
efficiency of digital signal processing algorithms. Information systems and medicine,
2024.
4. Kadyrov J., Tillyaev F. Methods for analyzing biomedical signals based on
artificial intelligence. Innovative technologies and health care, 2023.
5. Yusupova G., Saidov R. Processing biomedical signals using deep learning
algorithms. Science and technology, 2024.
6. Najmiddinov S., Torakulov E. Digital processing of real-time biomedical
signals in artificial intelligence. Medicine and informatics, 2023.
7. Abdullayev M., Israilov K. Algorithms for automatic analysis of biomedical
signals using artificial intelligence. Modern medical technologies, 2024.
