EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification

We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption. The proposed architecture leverages the energy efficiency of transistors operating in the subthreshold region, eliminating the need for analog-to-digital converters (ADC) and static random access memory (SRAM). The system design includes a novel analog sequential Multiply-Accumulate (MAC) circuit that mitigates process, supply voltage, and temperature variations. Experimental evaluations on PhysioNet's MIT-BIH and PTB Diagnostics datasets demonstrate the effectiveness of the proposed method, achieving average balanced accuracy of 95% and 94.25% for intra-patient arrhythmia classification and myocardial infarction (MI) classification, respectively. This innovative approach presents a promising avenue for developing low-power arrhythmia classification systems with enhanced accuracy and transferability in biomedical applications.Comment: Accepted on IEEE Biomedical Circuits and Systems (BioCAS) 202

Karşıt Akışlı Ranque– Hilsch Vorteks Tüpünün Makine Öğrenmesi Metotları ile Performans Analizi

Bu çalışmada, basınçlı oksijen gazı kullanarak aynı anda hem soğutma hem de ısıtma yapabilen ve kontrol vanası dışında hareketli parçası bulunmayan basit borudan meydana gelen Karşıt Akışlı Ranque-Hilsch Vorteks Tüp (KARHVT) kullanılmıştır. KARHVT’nün tasarımında boru 7 mm iç çapında ve 100 mm gövde uzunluğunda imal edilmiştir. KARHVT’ünde, nozul olarak pirinç, çelik, alüminyum ve polyamid üretilen 2, 3, 4, 5 ve 6 orfisli malzemesi kullanılmıştır. Yapılan deneylerde sıcak akışkan tarafında bulunan kontrol vanası tam açık konumda bırakılmış olup, giriş basıncı ilk 150 kPA olarak ayarlanmıştır. Daha sonra 50 kPa aralıklarla 700 kPa kadar veriler alınmıştır. KARHVT’de çıkan soğuk akışın sıcaklığı (Tsoğ) ve çıkan sıcak akış sıcaklığı (Tsck) ile arasındaki fark (ΔT) cinsinden bulunarak KARHVT’ün performansı optimizasyonu yapılmıştır. KARHVT’nün performansının optimizasyonunu, makine öğrenimi metotlarından Lineer Regresyon (LR), Regresyon Ağaçları (RA) ve Ağaç Toplulukları (AT) yöntemleri kullanılarak literatürdeki eksikliğin tamamlanması amaçlanmıştır. Çalışmada makine öğrenimi metotlarının her birinin analizi için, tüm verinin %80’i eğitim verisi, tüm verinin %20’si ise test verisi olarak kullanılmıştır. Çalışmanın sonunda eğitilen modeller sonunda gerçekleşen tahmin sonuçları ile gerçek deney sonuçları karşılaştırılarak yorumlanmıştır

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision trees to enable resource-efficient classification on a neural implant. By integrating model compression with probabilistic routing and implementing cost-aware learning, our proposed model could significantly reduce the memory and hardware cost compared to state-of-the-art models, while maintaining the classification accuracy. We trained the resource-efficient oblique tree with power-efficient regularization (ResOT-PE) on three neural classification tasks to evaluate the performance, memory, and hardware requirements. On seizure detection task, we were able to reduce the model size by 3.4X and the feature extraction cost by 14.6X compared to the ensemble of boosted trees, using the intracranial EEG from 10 epilepsy patients. In a second experiment, we tested the ResOT-PE model on tremor detection for Parkinson's disease, using the local field potentials from 12 patients implanted with a deep-brain stimulation (DBS) device. We achieved a comparable classification performance as the state-of-the-art boosted tree ensemble, while reducing the model size and feature extraction cost by 10.6X and 6.8X, respectively. We also tested on a 6-class finger movement detection task using ECoG recordings from 9 subjects, reducing the model size by 17.6X and feature computation cost by 5.1X. The proposed model can enable a low-power and memory-efficient implementation of classifiers for real-time neurological disease detection and motor decoding

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs

Bewegungsdekodierung für elektrophysiologisch gestützte intelligente adaptive tiefen Hirnstimulation bei der Parkinson-Krankheit

Deep Brain stimulation is an effective treatment for movement disorders such as Parkinson’s disease or essential tremor. Current therapy protocols do not adjust in real-time to the present need for treatment but instead rely on constant stimulation parameters. A novel concept called intelligent adaptive deep brain stimulation triggers stimulation based on decoding of a predefined state, such as movement, in a demand-driven way. Invasive Brain Computer Interfaces were previously presented for decoding behavioral states both using local field potential recordings from depth electrodes, primarily in movement disorder patients, and using electrocorticographic signals in epilepsy patients. Future brain implants may successfully treat different movement disorders using both modalities. A systematic brain signal decoding comparison of the two recording sites within patients was lacking. In this work, we analyzed invasive intraoperative recordings from Parkinson’s disease patients undergoing deep brain stimulation therapy. Subthalamic local field potentials and simultaneous electrocorticographic signals were recorded while the patients were performing a hand-gripping force task. We used these signals to develop a real-time-enabled feature estimation and decoding framework and investigated different hyperparameter-optimized machine learning approaches for the prediction of movement strength. We identified optimal temporal, spatial, and oscillatory decoding components. Our analysis showed for the first time that movement decoding performances of cortical recordings were superior to subcortical ones using different machine learning methods. We found that gradient-boosted decision trees showed the best performances for electrocorticographic recordings, while Wiener filters were optimal for subthalamic signals. Models from single electrode contacts were better performing than methods that combine data from multiple contacts. Decoding performances were negatively correlated to Parkinson's disease-specific symptom scores. Previously, subthalamic beta oscillations were reported to reflect Parkinson’s disease symptom severity, here we found that decoding performances were negatively correlated to elevated subthalamic beta oscillations. Additionally, we developed a movement decoding network that predicted contact-specific movement decoding performances using functional and structural connectivity profiles. In conclusion, we propose a computational framework based on invasive neurophysiology for brain signal decoding and highlight interactions of decoding performances with Parkinson’s disease symptom states, pathological symptom biomarkers, and whole-brain connectivity. This thesis, therefore, constitutes a significant contribution to the development of intelligent personalized medicine for adaptive deep brain stimulation.Tiefe Hirnstimulation ist eine effektive Behandlung von Bewegungsstörungen wie bei der Parkinson-Krankheit oder dem Essentiellen Tremor. Derzeitige Protokolle passen sich nicht in Echtzeit dem aktuellen Behandlungsbedarf an, sondern beruhen auf konstanten Stimulationsparametern. In einem neuen Therapieverfahren, der „intelligenten adaptiven tiefen Hirnstimulation“, wird die Stimulation bedarfsgerecht anhand eines vordefinierten Zustands, wie beispielsweise der Bewegung, angepasst. Invasive Brain Computer Interfaces konnten in vorigen Studien Verhaltenszustände mit elektrophysiologischen Aufnahmen dekodieren. Hier wurden entweder lokale Feldpotentiale, abgeleitet von Elektroden in tiefen Hirnregionen bei Patient*innen mit Bewegungsstörungen, oder elektrokortikographische Signale, bei Epilepsie-Patient*innen, verwendet. Beide Signal-Modalitäten könnten für zukünftige Hirnimplantate genutzt werden. Ein systematischer Vergleich der jeweiligen Dekodierleistung wurde bei denselben Patient*innen bisher nicht durchgeführt. Hier analysierten wir deshalb intraoperative Aufzeichnungen subthalamischer lokaler Feldpotentiale und gleichzeitige elektrokortikographische Ableitungen von Parkinson-Patient*innen während der Implantation des tiefen Hirnstimulators. Die Patient*innen führten Handbewegungen mit unterschiedlicher Greifkraft aus. Mittels echtzeitfähiger Feature Berechnung und Dekodierung untersuchten wir verschiedene Hyperparameter-optimierte maschinelle Lernverfahren zur Vorhersage der Bewegungsstärke. Wir identifizierten optimale temporale, oszillatorische und lokalisationsspezifische Parameter der Dekodierung. Unsere Studie zeigt zum ersten Mal, dass die Dekodierleistung von kortikalen gegenüber subkortikalen Signalen anhand von verschiedenen maschinellen Lernmethoden deutlich überlegen war. Gradient-boosted decision trees waren für elektrokortikographische Aufzeich-nungen die beste Dekodiermethode, während Wiener Filter für subthalamische Signale am geeignetsten waren. Modelle aus einzelnen Elektrodenkontakten zeigten bessere Dekodierleistungen als Modelle die Daten mehrerer Kontakte kombinierten. Die Dekodierleistung korrelierte negativ mit der Parkinson-Symptomschwere, und korrelierte zusätzlich negativ mit erhöhten subthalamischen Beta-Oszillationen, von denen bereits berichtet wurde, dass sie den Parkinson-Schweregrad widerspiegeln. Zusätzlich entwickelten wir ein Netzwerk für die Vorhersage der kontaktspezifischen Dekodierleistungen anhand von funktionellen und strukturellen Konnektivitätsprofilen. Zusammenfassend stellen wir ein computerbasiertes, neurophysiologisches Framework für die invasive Hirnsignal-Dekodierung vor. Wechselwirkungen der Dekodierleistung wurden mit der Parkinson-Symptomschwere, elektrophysiologischen Biomarkern pathologischer Symptome und der Konnektivität des gesamten Gehirns identifiziert. Diese Dissertation unterstützt daher die Entwicklung intelligenter, personalisierter Medizin für die adaptive tiefe Hirnstimulation

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs

Energy-Efficient Classification for Resource-Constrained Biomedical Applications

Biomedical applications often require classifiers that are both accurate and cheap to implement. Today, deep neural networks achieve the state-of-the-art accuracy in most learning tasks that involve large data sets of unstructured data. However, the application of deep learning techniques may not be beneficial in problems with limited training sets and computational resources, or under domain-specific test time constraints. Among other algorithms, ensembles of decision trees, particularly the gradient boosted models have recently been very successful in machine learning competitions. Here, we propose an efficient hardware architecture to implement gradient boosted trees in applications under stringent power, area, and delay constraints, such as medical devices. Specifically, we introduce the concepts of asynchronous tree operation and sequential feature extraction to achieve an unprecedented energy and area efficiency. The proposed architecture is evaluated in automated seizure detection for epilepsy, using 3074 h of intracranial EEG data from 26 patients with 393 seizures. Average F1 scores of 99.23% and 87.86% are achieved for random and block-wise splitting of data into train/test sets, respectively, with an average detection latency of 1.1 s. The proposed classifier is fabricated in a 65-nm TSMC process, consuming 41.2 nJ/class in a total area of 540×1850 μm^2 . This design improves the state-of-the-art by 27× reduction in energy-area-latency product. Moreover, the proposed gradient-boosting architecture offers the flexibility to accommodate variable tree counts specific to each patient, to trade the predictive accuracy with energy. This patient-specific and energy-quality scalable classifier holds great promise for low-power sensor data classification in biomedical applications

Energy-Efficient Classification for Resource-Constrained Biomedical Applications

Biomedical applications often require classifiers that are both accurate and cheap to implement. Today, deep neural networks achieve the state-of-the-art accuracy in most learning tasks that involve large data sets of unstructured data. However, the application of deep learning techniques may not be beneficial in problems with limited training sets and computational resources, or under domain-specific test time constraints. Among other algorithms, ensembles of decision trees, particularly the gradient boosted models have recently been very successful in machine learning competitions. Here, we propose an efficient hardware architecture to implement gradient boosted trees in applications under stringent power, area, and delay constraints, such as medical devices. Specifically, we introduce the concepts of asynchronous tree operation and sequential feature extraction to achieve an unprecedented energy and area efficiency. The proposed architecture is evaluated in automated seizure detection for epilepsy, using 3074 h of intracranial EEG data from 26 patients with 393 seizures. Average F1 scores of 99.23% and 87.86% are achieved for random and block-wise splitting of data into train/test sets, respectively, with an average detection latency of 1.1 s. The proposed classifier is fabricated in a 65-nm TSMC process, consuming 41.2 nJ/class in a total area of 540×1850 μm^2 . This design improves the state-of-the-art by 27× reduction in energy-area-latency product. Moreover, the proposed gradient-boosting architecture offers the flexibility to accommodate variable tree counts specific to each patient, to trade the predictive accuracy with energy. This patient-specific and energy-quality scalable classifier holds great promise for low-power sensor data classification in biomedical applications