Quasi-Periodicities Detection Using Phase-Rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor

Phase-rectified signal averaging (PRSA) has been known to be a useful method to detect periodicities in non-stationary biological signals. Determination of quasi-periodicities in electroencephalogram (EEG) is a candidate for quantifying the changes of depth of anesthesia (DOA). In this paper, DOA monitoring capacity of periodicities detected using PRSA were quantified by assessing EEG signals collected from 56 patients during surgery. The method is compared to sample entropy (SampEn), detrended fluctuation analysis (DFA) and permutation entropy (PE). The performance of quasi-periodicities defined by acceleration capacity (AC) and deceleration capacity (DC) was tested using the area under the receiver operating characteristic curve (AUC) and Pearson correlation coefficient. During the surgery, a significant difference (p < 0.05) in the quasi-periodicities was observed among three different stages under general anesthesia. There is a larger mean AUC and correlation coefficient of quasi-periodicities compared to SampEn, DFA and PE using expert assessment of conscious level (EACL) and bispectral index (BIS) as the gold standard, respectively. Quasi-periodicities detected using PRSA in EEG signals are powerful monitor of DOA and perform more accurate and robust results compared to SampEn, DFA and PE. The results do provide a valuable reference to researchers in the filed of clinical applications.10.13039/501100003711-Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by the Ministry of Science and Technology; 10.13039/501100001809-National Natural Science Foundation of China

Klasifikasi Penyakit Gagal Jantung Kongestif Menggunakan Artificial Neural Network (ANN) Berdasarkan Ekstraksi Fitur Multifractal Detrended Fluctuation Analysis (MFDFA) Pada Variabilitas Denyut JantungKlasifikasi Penyakit Gagal Jantung Kongestif Menggunakan Artificial Neural Network (ANN) Berdasarkan Ekstraksi Fitur Multifractal Detrended Fluctuation Analysis (MFDFA) Pada Variabilitas Denyut Jantung

Gagal Jantung Kongestif adalah gejala dan tanda-tanda yang muncul pada pasien yang hatinya tidak mampu menjaga fungsi peredaran darah yang cukup untuk memasok jaringan tubuh dengan oksigen dalam semua keadaan. Kondisi tersebut dapat terjadi sementara atau permanen. Tekanan darah tinggi merupakan penyebab utama gagal jantung kongestif. penyakit jantung dan diabetes juga faktor besar yang mendasari penyebab gagal jantung. Jantung Koroner dan Gagal Jantung merupakan bagian dari penyakit Cardiovaskular (CVD). Berdasarkan data WHO pada tahun 2008 sebanyak 17,3 juta manusia meninggal akibat CVD dan meningkat di tahun 2012 sebanyak 17,5 juta. Di Indonesia sendiri berdasarkan data dari Menteri Kesehatan RI prevalensi penyakit gagal jantung Indonesia tahun 2013 sebesar 0,13% atau diperkirakan sekitar 229.696 orang, sedangkan berdasarkan diagnosis dokter/ gejala sebesar 0,3% atau diperkirakan sekitar 530.068 orang. Penelitian ini menggunakan metode Multifractal Detrended Fluctuation Analysis (MFDFA) pada sinyal HRV berdasarkan denyut jantung untuk mengatasi adanya skala invarian yang memiliki variasi spasial dan temporal. Hasil dari ekstraksi fitur Multifractal Detrended Fluctuation Analysis dilakukan klasifikasi Artificial Neural Network untuk mengetahui perbedaan sinyal HRV pada pasien normal dan pasien penyakit gagal jantung kongestif. Hasil klasifikasi dari ektstaksi fitur MFDFA menghasilkan akurasi terbaik sebesar 67.24%, akan tetapi hasil akurasi tersebut masih kurang baik karena dalam hasilnya untuk label chf1 dan chf2 hasil klasifikasi hanya beberapa kecil data saja yang diklasfikasikan dengan benar dan hasil ROC nya bernilai 0.694 yang dapat dikategorikan sebagai klasifikasi yang buruk Hasil dari penelitian ini diharapkan dapat digunakan dalam penelitian selanjutnya dengan menggunakan metode klasifikasi lainnya dan dapat bermanfaat di dunia medis untuk mendiagnosa penyakit gagal jantung kongestif pada pasien agar segera dilakukan tindakan preventif. ================================================================================== Congestive Heart Failure is a symptom and signs that appear in patients whose heart is unable to maintain adequate circulatory function to supply body tissue with oxygen in all circumstances. Such conditions may occur temporarily or permanently. High blood pressure is a major cause of congestive heart failure. Heart disease and diabetes are also major factors that underlie the causes of heart failure. Coronary Heart and Heart Failure are part of Cardiovascular disease (CVD). Based on WHO data in 2008 as many as 17.3 million people died from CVD and increased in 2012 as much as 17.5 million. In Indonesia itself, based on data from the Minister of Health of Indonesia, the prevalence of heart failure in 2013 is 0.13% or an estimated 229,696 people, while physician / symptom is 0.3% or approximately 530,068 people. This study used the Multifracted Detrended Fluctuation Analysis (MFDFA) method on HRV signal based on heart rate to overcome the existence of invariant scale having spatial and temporal variation. The result of feature extraction of Multifractal Detrended Fluctuation Analysis was classified with Artificial Neural Network to know the difference of HRV signal in normal patients and patients with congestive heart failure. The classification results of the feature extraction of MFDFA yielded the best accuracy of 67.24%, but the result of the accuracy is still not good because in result for label chf1 and chf2 result of classification only few small data are classified correctly and the result of ROC is 0.694 which can be categorized as bad classification. The results of this study are expected to be used in subsequent research using other classification methods and can be useful in the medical world to diagnose congestive heart failure in patients for immediate preventive action

Symbolic Dynamics applied to Electroencephalographic signals to Predict Response to Noxious Stimulation during Sedation-Analgesia

The level of sedation in patients undergoing medical procedures evolves continuously since the effect of the anesthetic and analgesic agents is counteracted by noxious stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. In this project, a methodology based on non-linear techniques signal processing algorithms was developed and applied to the electroencephalogram (EEG) for predicting responses to noxious stimulation during Sedation-Analgesia. Two types of stimuli were performed by the anesthesiologist during the surgery sessions, RSS (Ramsay Sedation Scale) and GAG (gag reflex). These sedation scales are considered gold standard. In this work, the scope of the project includes: EEG preprocessing, processing and analysis of the mentioned signals. The methodology included an EEG signal preprocessing, a time-domain and frequency-domain analysis, the development and application of non-linear techniques, a statistical analysis and finally the validation of the results. Symbolic dynamics methodology, already applied to other kind of signals, was used as a non-linear technique. The aim was to extract a set of patterns from the EEG obtained through two proposed non-linear algorithms. The symbolic dynamics consists of the transformation of the time signal in a series of symbols by an algorithm. From these new series, words of three symbols were constructed with one symbol delay and their occurrence probability was evaluated in the signals variables. Base on this, the Shannon and Rényi entropies were applied to estimate the complexity of the distribution of the variables. Moreover, thresholds on probabilities were used to construct new variables. The analysis was applied to the EEG filtered according to the characteristic frequency bands (EEG rhythms). The parameters involved in the algorithms were statistically adjusted in order to better characterize the nociceptive response. Variables obtained from linear and non-linear methodologies were submitted to a statistical analysis using a nonparametric test and a linear discriminant analyses to assess the quality of the classification. The leaving-one-out method was used as validation criteria. New defined variables were able to describe the different states with p-value 60%, Sen > 60% and Pk > 0,6. This signal processing methodology technically contributes to the prediction of anesthesia depth level during Sedation-Analgesia

Novel non-linear approaches to understanding the dynamic brain: knowledge from rsfMRI and EEG studies

Advances in neuroimaging techniques have been critical to identifying new biomarkers for brain diseases. Resting State Functional Magnetic Resonance Imaging (rsfMRI) non-invasively quantifies the Blood Oxygen Level Dependent (BOLD) signal across brain regions with high spatial resolution; whilst temporal resolution of Electroencephalography (EEG) in measuring the brain's electrical response is unsurpassed. Most of the statistical and machine learning methods used to analyze rsfMRI and EEG data, are static and linear, fail to capture the dynamics and complexity of the brain, and are prone to residual noise. The general goals of this thesis dissertation are i) to provide methodological insight by proposing a statistical method namely point process analysis (PPA) and a machine learning (ML) multiband non-linear EEG method. These methods are especially useful to investigate the brain configuration of older participants and individuals with neurodegenerative diseases, and to predict age and sleep quality; and ii) to share biological insights about synchronization between brain regions (i.e., functional connectivity and dynamic functional connectivity) in different stages of mild cognitive impairment and in Alzheimer's disease. The findings, reported and discussed in this thesis, open a path for new research ideas such as applying PPA to EEG data, adjusting the non-linear ML algorithm to apply it to rsfMRI and use these methods to better understand other neurological diseases. Los avances en las técnicas de neuroimagen han sido fundamentales para identificar nuevos biomarcadores de enfermedades cerebrales. La resonancia magnética funcional en estado de reposo (rsfMRI) cuantifica de forma no invasiva la señal dependiente del nivel de oxígeno en sangre (BOLD) en todas las regiones del cerebro con una alta resolución espacial, mientras que la resolución temporal de la electroencefalografía (EEG) para medir la respuesta eléctrica del cerebro es insuperable. La mayoría de los métodos estadísticos y de aprendizaje automático utilizados para analizar datos de rsfMRI y EEG son estáticos y lineales, no captan el dinamismo y la complejidad del cerebro y son propensos al ruido residual. Los objetivos generales de esta tesis doctoral son i) proporcionar una visión metodológica proponiendo un método estadístico, llamado análisis por proceso de puntos (PPA), y un método de aprendizaje automático (ML) multibanda no lineal de EEG. Estos métodos son especialmente útiles para investigar la configuración cerebral de participantes de edad avanzada y de individuos con enfermedades neurodegenerativas, y para predecir la edad y la calidad del sueño; y ii) compartir conocimientos biológicos sobre la sincronización entre regiones cerebrales (es decir, la conectividad funcional y la conectividad funcional dinámica) en diferentes etapas del deterioro cognitivo leve y en la enfermedad de Alzheimer. Los hallazgos, comunicados y discutidos en esta tesis, abren un camino para nuevas ideas de investigación, como la aplicación de PPA a datos de EEG, el ajuste del algoritmo ML no lineal para aplicarlo a rsfMRI y el uso de estos métodos para comprender mejor otras enfermedades neurológicas