Electroencephalographic Changes in Pregnant Women with Hyperemesis Gravidarum: A Case-Control Study.

OBJECTIVE: Electroencephalogram (EEG), which is frequently used in the clinical practice of neurology, has also been investigated in eating disorders and some cortical dysfunctions have been reported. Based on this, we aimed to investigate EEG changes in pregnant women with hyperemesis gravidarum (HEG). MATERIALS AND METHODS: This case-control study was conducted on 66 pregnant women who applied to the Umraniye Training and Research Hospital, Department of Obstetrics and Gynecology. The study group consisted of 34 pregnant women diagnosed with HEG. The control group consisted of 32 healthy pregnant women who were matched with the HEG group in terms of age and gestational week. EEGs of the participants were performed with a Micromed Brain Rapid EEG device in the Neurology Department of Umraniye Training and Research Hospital. In EEGs, all channels were selected as bipolar and samples of 18 channels (Fp2-F4, F4-C4, C4-P4, P4-O2, Fp2-F8, F8-T4, T4-T6, T6-O2, Fz-Cz, Cz-Pz, Fp1-F3, F3-C3, C3-P3, P3-O1, Fp1-F7, F7-T3, T3-T5, and T5-O1) were obtained. EEG signals were sampled with a sampling frequency of 200 Hz and digitized with 12-bit resolution. EEG signals were converted to EDF (European Data Format) extension files using the MATLAB software program and analyzed using statistical features on the time and frequency axis. HEG and control groups were compared in terms of signals obtained from these 18 selected channels. RESULTS: Both groups were similar in terms of mean age, gestational age, and parity (p>0.05). Among the 18 channels, significant changes were detected between the two groups only in the theta, beta, and gamma bands in the C4-P4 channel and the delta, beta, and gamma bands in the T4-T6 channel (p<0.05). No significant changes were detected in the channels and bands. CONCLUSION: Theta, beta, and gamma band abnormalities in the centro-parietal area of the right hemisphere and delta, beta, and gamma band abnormalities in the temporal area of the right hemisphere were observed on HEG. However, it is unclear whether abnormalities in EEG are primary changes responsible for the development of HEG or secondary to metabolic and hormonal changes resulting from HEG itself.

The effects of hypercortisolism on the frequency and magnitude of sleep EEG waves in patients with Cushing syndrome: A spectral analysis study.

OBJECTIVES: Our aim was to investigate the effects of endogenous chronic hypercortisolism on sleep electroencephalogram (EEG) and differences between the adrenocorticotropic hormone (ACTH)-dependent and independent Cushing Syndrome (CS) patients through a sleep spectral analysis program. METHODS: A total of 32 patients diagnosed as having endogenous CS (12 ACTH-dependent and 20 ACTH-independent) and a control group comprising 16 healthy individuals were included in the study. Polysomnographic analysis was performed. Blood samples were collected at 08:00 AM for analysis of ACTH and basal cortisol, and at 00:00 AM for midnight cortisol levels. The frequency and power of the slow wave activity (SWA), theta, alpha, and beta waves of the first and last non-rapid eye movement (NREM) cycles were measured with a spectral analysis program. RESULTS: The CS patient group had higher SWA power, especially in the first NREM cycle. In the ACTH-dependent group, SWA maximum and mean power values were higher in the frontal channels in the first NREM, compared to the last NREM sleep stage (p<0.05). CONCLUSION: Cortisol has been found to be associated with SWA waves, making these waves higher in power, especially in the first NREM phase. This difference was much less pronounced in the final NREM sleep stage. The difference between the first and last NREM sleep stages with respect to the power of SWA in the frontal channel in the ACTH-dependent group suggests that not only cortisol but also high levels of ACTH affect the power of slow waves during sleep.

SERS-based sensor with a machine learning based effective feature extraction technique for fast detection of colistin-resistant Klebsiella pneumoniae.

Colistin-resistant Klebsiella pneumoniae (ColR-Kp) causes high mortality rates since colistin is used as the last-line antibiotic against multi-drug resistant Gram-negative bacteria. To reduce infections and mortality rates caused by ColR-Kp fast and reliable detection techniques are vital. In this study, we used a label-free surface-enhanced Raman scattering (SERS)-based sensor with machine learning algorithms to discriminate colistin-resistant and susceptible strains of K. pneumoniae. A total of 16 K. pneumoniae strains were incubated in tryptic soy broth (TSB) for 4 h. Collected SERS spectra of ColR-Kp and colistin susceptible K. pneumoniae (ColS-Kp) have shown some spectral differences that hard to discriminate by the naked eye. To extract discriminative features from the dataset, autoencoder and principal component analysis (PCA) that extract features in a non-linear and linear manner, respectively were performed. Extracted features were fed into the support vector machine (SVM) classifier to discriminate K. pneumoniae strains. Classifier performance was evaluated by using features extracted by each feature extraction techniques. Classification results of SVM classifier with extracted features by an autoencoder (autoencoder-SVM) has shown better performance than SVM classifier with extracted features by PCA (PCA-SVM). The accuracy, sensitivity, specificity, and area under curve (AUC) value of the autoencoder-SVM model were found as 94%, 94.2%, 93.8%, and 0.98, respectively. Furthermore, the autoencoder-SVM model has demonstrated statistically significantly better classifier performance than PCA-SVM in terms of accuracy and AUC values. These results illustrate that non-linear features can be more discriminative than linear ones to determine SERS spectral data of antibiotic-resistant and susceptible bacteria. Our methodological approach enables rapid and high accuracy detection of ColR-Kp and ColS-Kp, suggesting that this can be a promising tool to limit colistin resistance.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques.

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.

The correlation between the electroencephalographic spectral analysis and salivary cortisol rhythm in insomnia.

OBJECTIVE: Insomnia is a common sleep disorder that affects many adults either transiently or chronically. This study aimed to establish whether there is a relationship between the electroencephalographic (EEG) spectral analysis and salivary cortisol levels in insomnia and compared to healthy controls. MATERIALS AND METHODS: This case-control study included 15 insomnia patients and 15 healthy control subjects. Insomnia was determined according to the International Classification of Headache Disorders III diagnostic criteria. The EEG data were collected and processed with MATLAB software. Blood and salivary samples were taken for hematological and biochemical measurements. Salivary cortisol levels were calculated and compared statistically with the healthy group. RESULTS: The mean age of the patients was 46.5 ± 11 years. The salivary cortisol levels at 18:00 and 24:00 were found higher in the insomnia than in the healthy subjects (respectively, 0.12 (0.11) µg/dl, 0.07 (0.02) µg/dl). But this difference was not statistically significant (p > 0.05). No significant difference was observed in the spectral analysis of patients between the frontal, central, and occipital channel (p > 0.05). However, in the correlation between the frontal channel spectral analysis and at the 24:00 salivary cortisol of patient and control group, DeltaGmax (p = 0.002), DeltaGmean (p = 0.019) and, in the correlation with 18:00 salivary cortisol DeltaGmax (p = 0.010), were positively correlated. CONCLUSION: In this study, no significant difference was found in spectral analysis and salivary cortisol levels in insomnia patients, but at 18:00 and 24:00, cortisol levels were correlated positively with theta and delta waves in EEG spectral analysis in some channels.

Evaluation of Two Types of Drug Treatment with QEEG in Children with ADHD.

AIMS: The aim of this study is to evalute the effects of methylphenidate and atomoxetine treatments on electroencephalography (EEG) signals in volunteer children diagnosed with Attention Deficit and Hyperactivity Disorder(ADHD). METHODS: The study contained 40 children all of whom were between the ages of 7 and 17. The participants were classified into two groups as ADHD (n=20), which was in itself divided into two groups as ADHD-MPH (ADHD- Metylphenidate treatment) (n=10) and as ADHD-ATX (ADHD-Atomoxetin treatment) (n=10), and one control group (n=20). Following the first EEG recordings of the ADHD group, long-acting methylphenidate dose was applied to one ADHD group and atomoxetine dose was applied to the other ADHD group. The effect of optimal dosage is about for 4-6 weeks in general. Therefore, the response or lack of response to the treatment was evaluated three months after the beginning of the treatment.After methylphenidate and atomoxetine drug treatment, in order to obtain mean and maximum power values for delta, theta, alpha and beta band, the EEG data were analyzed. RESULTS: The EEG power spectrum densities in all the bands yielded similar findings in both methylphenidate and atomoxetine. Although statistically significant frequency values of the electrodes were amplitude and maximally varied, in general, they appeared mostly at both frontal and temporal regions for methylphenidate and atomoxetine. CONCLUSION: Especially, after atomoxetine treatment, Quantitative Electroencephalography (QEEG) rates at frontal area electrodes were found statistically more significant than methylphenidate QEEG rates. What has been researched in this study is not only whether QEEG is likely to support the diagnosis, but whether changes on QEEG by treatment may be related to the severity of ADHD as well.

Investigation of attention deficit hyperactivity disorder (ADHD) sub-types in children via EEG frequency domain analysis.

AIM OF THE STUDY: To investigate the frequency domain effects and changes in electroencephalography (EEG) signals in children diagnosed with attention deficit hyperactivity disorder (ADHD). PATIENTS AND METHODS: The study contains 40 children. All children were between the ages of 7 and 12 years. Participants were classified into four groups which were ADHD (n=20), ADHD-I (ADHD-Inattentive type) (n=10), ADHD-C (ADHD-Combined type) (n=10), and control (n=20) groups. In this study, the frequency domain of EEG signals for ADHD, subtypes and control groups were analyzed and compared using Matlab software. The mean age of the ADHD children's group was 8.7 years and the control group 9.1 years. RESULTS: Spectral analysis of mean power (µV2) and relative-mean power (%) was carried out for four different frequency bands: delta (0--4 Hz), theta (4--8 Hz), alpha (8--13 Hz) and beta (13--32 Hz). The ADHD and subtypes of ADHD-I, and ADHD-C groups had higher average power value of delta and theta band than that of control group. However, this is not the case for alpha and beta bands. Increases in delta/beta ratio and statistical significance were found only between ADHD-I and control group, and in delta/beta, theta/delta ratio statistical significance values were found to exist between ADHD-C and control group. CONCLUSION: EEG analyzes can be used as an alternative method when ADHD subgroups are identified.

Effect of chewing on dental patients with total denture: an experimental study.

In this study, we have explored the prospect of assessing and following level of total denture adaptation by use of EMG signals recorded during gum chewing. Total of 14 edentulous patients, 6 women and 8 men, with an average age of 63±9 years, were recruited. Separate EMG recordings were obtained from left and right temporalis and masseter muscles of the patients for a period of 10 seconds, while they were chewing a sugar-free gum on their left and right sides. EMG recordings were repeated at three times: before, right after, and six months after the placement of the denture. We have tried to standardize environmental and individual factors during EMG recordings. The EMG data have been pre-processed and analyzed using Discrete Wavelet Transform (DWT) and obtained features were statistically evaluated using the paired sample t-test. Chewing activity on the right and left side is analyzed by making comparisons of muscle activity between before and right-after cases and before and six-months-after denture fixation cases. A comparison between right and left side mastication is also made at different time points. We have suggested and implemented a new test and comparison procedure in order to assess adaptation to denture fixation using EMG analysis. In this study, the results indicate that DWT based EMG analysis is instrumental in evaluating denture adaptation and as time progresses the adaptation to denture and hence chewing efficiency increases in patients with total denture replacement.

Denoising of arterial and venous Doppler signals using discrete wavelet transform: effect on clinical parameters.

In this paper, the effects of a wavelet transform based denoising strategy on clinical Doppler parameters are analyzed. The study scheme included: (a) Acquisition of arterial and venous Doppler signals by sampling the audio output of an ultrasound scanner from 20 healthy volunteers, (b) Noise reduction via decomposition of the signals through discrete wavelet transform, (c) Spectral analysis of noisy and noise-free signals with short time Fourier transform, (d) Curve fitting to spectrograms, (e) Calculation of clinical Doppler parameters, (f) Statistical comparison of parameters obtained from noisy and noise-free signals. The decomposition level was selected as the highest level at which the maximum power spectral density and its corresponding frequency were preserved. In all subjects, noise-free spectrograms had smoother trace with less ripples. In both arterial and venous spectrograms, denoising resulted in a significant decrease in the maximum (systolic) and mean frequency, with no statistical difference in the minimum (diastolic) frequency. In arterial signals, this leads to a significant decrease in the calculated parameters such as Systolic/Diastolic Velocity Ratio, Resistivity Index, Pulsatility Index and Acceleration Time. Acceleration Index did not change significantly. Despite a successful denoising, the effects of wavelet decomposition on high frequency components in the Doppler signal should be challenged by comparison with reference data, or, through clinical investigations.

Investigation of the arterial stiffness on renal artery Doppler sonograms.

This study aims to identify the effect of stiffness on renal artery Doppler sonograms by using a curve fitting approach. The study includes 20 patients with suspected renovascular hypertension. Each patient underwent recordings during renal artery examinations before and after administration of Captopril 25 mg (Kapril tablet, MN Pharmaceuticals, Istanbul, Turkey). Doppler signals were analyzed with short time Fourier transformation, followed by curve fitting to sonograms using Savitzky-Golay method. Doppler parameters obtained from pre- and post-Captopril curve-fitted sonograms were statistically compared with "Paired-Samples t-Test". Captopril administration resulted in a significant decrease in the mean frequency and a significant increase in Pulsatility Index of the periods. No significant difference was detected between maximum systolic and minimum diastolic frequencies, Resistivity Index, and the Acceleration Index. The computational approach in this study appears to be promising.

A classification system for stenosis from mitral valve Doppler signals using adaptive network based fuzzy inference system.

In this study, cardiac Doppler parameters were studied in 60 patients with mitral valve stenosis and compared with 41 ages and sex matched healthy controls. Firstly, the sonograms which represent the changes in Doppler frequency with respect to time were performed from mitral valve Doppler signals using short time Fourier transformation (STFT) method. Secondly, the envelopes of these sonograms and data set depicted from sonogram envelopes were acquired. Finally, the processed data set are applied to the proposed adaptive network based fuzzy inference system (ANFIS) model has potential in classifying the mitral valve Doppler signals. This result confirms that our technique contribute to the detection of mitral valve stenosis and our method offers more reliable information than looking at the sonogram on the Doppler screen and making a decision from the visual inspection. The proposed ANFIS model combined the neural network with adaptive capabilities and qualitative approach of fuzzy logic. The obtained results show that 98% correct classification was achieved, whereas two false classifications have been observed for the test group of 101 people.

Analysis of the electrogastrogram using discrete wavelet transform and statistical methods to detect gastric dysrhythmia.

Electrogastrography (EGG) is a method of recording stomach electrical activity from cutaneous electrodes placed on the abdominal surface. Compared with other electrophysiological measurements, such as electrocardiography, the progress of the applicability of the EGG has been very slow. Unlike imaging or manometrical studies, stomach motility disorders are not diagnosed based only on abnormal EGG parameters. Limitations of EGG recording, processing, computation, acceptable normal parameters, technique and reading should be known to conduct subjective assessments when EGG is used to resolve stomach dysfunction. Therefore appropriate application of non-invasive EGG should go on providing more information and insight in understanding these limitations. And so the aim of this study were to contribute the evolution of the EGG to enter the clinical world as a routine check-up method and to develop new time-frequency analysis method for the detection of gastric dysrhythmia from the EGG.

A high-voltage pulse generation instrument for electrochemotherapy method.

Electrochemotherapy (ECT) is a new method that uses anticancer drugs delivery with intensive electrical pulses. Recently, ECT as the treatment method can be applied for basal cell and spin cell carcinoma and for melanoma metastases. In this paper, a new design of a high voltage pulse generator with variable output pulse magnitude, repetition frequency, and pulse duration is presented. Furthermore, it has presented the basic theory of ECT, the importance/advantages against other cancer treatment methods, the theoretical model of electroporated cell membrane, and the application ways of ECT method. The proposed instrument is suitable for effective drug delivery of ECT in anti-tumor treatment. Also, this instrument can be applied to gene transfer/therapy methods.

A programmable iontophoretic instrument and its application for local anesthesia before surgery in urology.

In this paper, for an alternative anesthesia before transurethral resection of bladder tumors (TUR-BT) an original equipment which is used iontophoresis method was implemented. The implemented instrument is used on five patients for TUR-BT operations. This equipment can be programmable between 0 and 15 mA current by 1 mA steps and with in time 0 - 59 min. Also, frequency value of applying currents can be changed in 1 - 3 KHz frequency range. The use of iontophoresis in urology is relatively new. This method recently has been used in urology because of its simple usage in applying local anesthesia, and an alternative for general anesthesia.