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Journal of Radio Electronics Online scientific journal · eISSN 1684-1719
2019, № 6 · BIOMEDICAL RADIO ELECTRONICS

Increasing detection efficiency of border mental disorders based on adaptive decomposition and cepstral analysis of speech signals

A.K. Alimuradov1,2, A.I. Tychkov1,2, P.P. Churakov1,2, A.P. Zaretskiy3,4, I.B. Prokhorov3,4, K.S. Mityagin3,4

1 Пензенский государственный университет, Penza, Russia
2 Penza State University, Penza, Russia
3 Московский физико-технический институт (государственный университет)
4 Moscow Institute of Physics and Technology Moscow Institute of Physics and Technology (State University), Dolgoprudny, Russia
Full text (PDF) DOI: 10.30898/1684-1719.2019.6.6 EDN TLIMAN

Abstract

The detection accuracy of borderline mental disorders depends on correct processing of speech signals. The main reason of low accuracy and large errors in measurements is associated with the use of inefficient and non-adaptive methods for processing of non-stationary speech signals. In this paper, the authors propose a method for increasing the detection efficiency of borderline mental disorders based on adaptive decomposition technology for non-stationary signals, namely, improved complete ensemble empirical mode decomposition with adaptive noise and mel-frequency cepstral analysis. A block diagram for the method and a brief mathematical description are presented. The research results are presented, on the basis of which it was concluded that the method proposed by the authors can successfully be tested in remote monitoring systems of psychogenic disorders to accelerate the treatment process.

  • речевой сигнал
  • пограничные психические расстройства
  • мел-частотные кепстральные коэффициенты
  • эмпирическая модовая декомпозиция сигналов с адаптивным шумом
  • speech signal
  • border mental disorders
  • mel-frequency cepstral coefficients
  • improved complete ensemble empirical mode decomposition with adaptive noise
Аннотация (на русском)

Повышение эффективности выявления пограничных психических расстройств на основе адаптивной декомпозиции и кепстрального анализа речевых сигналов

Точность выявления пограничных психических расстройств зависит от правильной обработки речевых сигналов. Основная причина низкой точности и больших погрешностей в измерениях связана с использованием неэффективных и неадаптивных методов обработки нестационарных речевых сигналов. В данной статье авторы предлагают метод повышения эффективности обнаружения пограничных психических расстройств, основанный на технологии адаптивной декомпозиции для нестационарных сигналов, а именно, улучшенную полностью ансамблевую эмпирическую модовую декомпозицию сигналов с адаптивным шумом и мел-частотным кепстральным анализом. Представлена структурная схема метода и краткое математическое описание. Представлены результаты исследований, на основании которых был сделан вывод о том, что предложенный авторами метод может быть успешно апробирован в системах дистанционного мониторинга психогенных нарушений для ускорения процесса лечения.

For citation

Alimuradov A.K., Tychkov A.I., Churakov P.P., Zaretskiy A.P., Prokhorov I.B., Mityagin K.S. Increasing detection efficiency of border mental disorders based on adaptive decomposition and cepstral analysis of speech signals // Journal of Radio Electronics. – 2019. – No. 6. https://doi.org/10.30898/1684-1719.2019.6.6

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