We have developed a single-breath-hold photoacoustic computed tomography (SBH-PACT) system to reveal detailed angiographic structures in human breasts. SBH-PACT features a deep penetration depth (4 cm in vivo) with high spatial and temporal resolutions (255 µm in-plane resolution and a 10 Hz 2D frame rate). By scanning the entire breast within a single breath hold (~15 s), a volumetric image can be acquired and subsequently reconstructed utilizing 3D back-projection with negligible breathing-induced motion artifacts. SBH-PACT clearly reveals tumors by observing higher blood vessel densities associated with tumors at high spatial resolution, showing early promise for high sensitivity in radiographically dense breasts. In addition to blood vessel imaging, the high imaging speed enables dynamic studies, such as photoacoustic elastography, which identifies tumors by showing less compliance. We imaged breast cancer patients with breast sizes ranging from B cup to DD cup, and skin pigmentations ranging from light to dark. SBH-PACT identified all the tumors without resorting to ionizing radiation or exogenous contrast, posing no health risks.

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Single-breath-hold photoacoustic computed tomography of the breast.

Epilepsy is one of the most prevalent neurological diseases among humans and can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to mitigate injuries, and can be used to aid the treatment of patients with epilepsy. The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem. However, little attention has been given for designing such models to adapt to the high inter-subject variability in EEG data. We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects. Proposed models achieve state-of-the-art performance for seizure prediction on the CHB-MIT-EEG dataset, demonstrating 88.81% and 91.54% accuracy respectively. In conclusion, the Siamese model trained on the proposed learning strategy is able to learn patterns related to patient variations in data while predicting seizures. Our models show superior performance for patient-independent seizure prediction, and the same architecture can be used as a patient-specific classifier after model adaptation. We are the first study that employs model interpretation to understand classifier behavior for the task for seizure prediction, and we also show that the MFCC feature map utilized by our models contains predictive biomarkers related to interictal and pre-ictal brain states.

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Deep Learning for Patient-Independent Epileptic Seizure Prediction Using Scalp EEG Signals

Automated inter-patient seizure detection using multichannel Convolutional and Recurrent Neural Networks

This paper aims to apply machine learning techniques to an automated epileptic seizure detection using EEG signals to help neurologists in a time-consuming diagnostic process We employ two approaches based on convolution neural networks (CNNs) and artificial neural networks (ANNs) to provide a probability of seizure occurrence in a windowed EEG recording of 18 channels In order to extract relevant features based on time, frequency, and time-frequency domains for these networks, we consider an improvement of the Bayesian error rate from a baseline Features of which the improvement rates are higher than the significant level are considered These dominant features extracted from all EEG channels are concatenated as the input for ANN with 7 hidden layers, while the input of CNN is taken as raw multi-channel EEG signals Using multi-concept of deep CNN in image processing, we exploit 2D-filter decomposition to handle the signal in spatial and temporal domains Our experiments based on CHB-MIT Scalp EEG Database showed that both ANN and CNN were able to perform with the overall accuracy of up to 9907% and F1-score of up to 7704% ANN with dominant features is more capable of detecting seizure events than CNN whereas CNN requiring no feature extraction is slightly better than ANN in classification accuracy

https://www.biorxiv.org/content/biorxiv/early/2019/07/15/702654.full.pdf

A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals

We propose a novel Coupled Hidden Markov Model (CHMM) to detect and localize epileptic seizures in clinical multichannel scalp electroencephalography (EEG) recordings. Our model captures the spatio-temporal spread of a seizure by assigning a sequence of latent states (i.e. baseline or seizure) to each EEG channel. The state evolution is coupled between neighboring and contralateral channels to mimic clinically observed spreading patterns. Since the latent state space is exponential, a structured variational algorithm is developed for approximate inference. The model is evaluated on simulated and clinical EEG from two different hospitals. One dataset contains seizure recordings of adult focal epilepsy patients at the Johns Hopkins Hospital; the other contains publicly available non-specified seizure recordings from pediatric patients at Boston Children’s Hospital. Our CHMM model outperforms standard machine learning techniques in the focal dataset and achieves comparable performance to the best baseline method in the pediatric dataset. We also demonstrate the ability to track seizures, which is valuable information to localize focal onset zones.

A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy

Purpose To design a non-patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy. Methods We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions. The performance of five different classifiers was analyzed using these features to design an automated detection system. Results Among the five classifiers, decision tree, k-nearest neighbor, and support vector machine performed with sensitivity (specificity) of 79% (81%), 75% (85%), and 80% (86%), respectively. The other two, linear discriminant algorithm and Naive Bayes classifiers, performed with sensitivity (specificity) of 54% (94%), 47% (96%), respectively. Conclusions The support vector machine-based seizure detection system showed better detection capability in terms of sensitivity and specificity measures as compared to linear discriminant algorithm, Naive Bayes, decision tree, and k-nearest neighbor classifiers. Conclusions Our study shows that a generalized system to detect the electrical onset of seizures in temporal lobe epilepsy using scalp-recorded EEG is possible. If confirmed on a larger data set, our findings may have significant implications for the management of seizures, especially in patients with drug-resistant epilepsy.

Improved Patient-Independent System for Detection of Electrical Onset of Seizures.

The elastic properties of tissue are related to tissue composition and pathological changes. It has been observed that many pathological processes increase the elastic modulus of soft tissue compared to normal. Ultrasound compression elastography is a method of characterization of elastic properties that has been the focus of many research efforts in the last two decades. In medical radiology, compression elastography is provided as an additional tool with ultrasound B-mode in the existing scanners, and the combined features of elastography and echography act as a promising diagnostic method in breast cancer detection. However, the full capability of the ultrasound elastography technique together with B-mode has not been utilized by novice radiologists due to the nonavailability of suitable, appropriately designed tissue-mimicking phantoms. Since different commercially available ultrasound elastographic scanners follow their own unique protocols, training novice radiologists is becoming cumbersome. The main focus of this work is to develop a tissue-like agar-based phantom, which mimics breast tissue with common abnormal lesions like fibroadenoma and invasive ductal carcinoma in a clinically perceived way and compares the sonographic and elastographic appearances using different commercially available systems. In addition, the developed phantoms are simulated using the finite-element method, and ideal strain images are generated. Strain images from experiment and simulation are compared based on image contrast parameters, namely contrast transfer efficiency (CTE) and observed strain, and they are in good agreement. The strain image contrast of malignant inclusions is significantly improved compared to benign inclusions, and the trend of CTE is similar for all elastographic scanners under investigation.

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Development of a training phantom for compression breast elastography-comparison of various elastography systems and numerical simulations.

This study aims to design a steady-state visual evoked potential (SSVEP) based, on-screen keyboard/speller system along with the integration of electrooculogram (EOG). The characters/targets were designed using the pattern reversal square checkerboard flickering visual stimuli. In this study, twenty-three characters were randomly selected and their corresponding visual stimuli were designed using five frequencies (6, 6.667, 7.5, 8.57 and 10 Hz). The keyboard layout was divided into nine regions and each region was identified by using the subject’s eye gaze information with the help of EOG data. The information from the EOG was used to locate the area on the visual keyboard/display, where the subject is looking. The region identification helps to use the same frequency valued visual stimuli more than once on the keyboard layout. In this proposed study, more targets were designed using less number of visual stimulus frequencies by integrating EOG with the SSVEP keyboard system. The multi-threshold algorithm and extended multivariate synchronization index (EMSI) method were used for eye gaze detection and SSVEP frequency recognition respectively. Ten healthy subjects were recruited for validating the proposed visual keyboard system.

Machireddy Ramasubba Reddy

Papers

Improved Patient-Independent System for Detection of Electrical Onset of Seizures.

Development of a training phantom for compression breast elastography-comparison of various elastography systems and numerical simulations.

A Brain Computer Interface Based Visual Keyboard System Using SSVEP and Electrooculogram