Abstract: A method and device for performing DNA sequencing and extracting structural information from unknown nucleic acid strands. The device includes a microwell structure, where identical DNA strands are immobilized within the microwell structure on a surface of a micro-bead, an active electrode or a porous polymer. The device further includes a CMOS-integrated semiconductor integrated circuit, where the CMOS-integrated semiconductor integrated circuit includes metal layers on a silicon substrate, where the metal layers form an active electrode biosensor. In addition, a sensing electrode is formed by creating openings in a passivation layer of the CMOS-integrated semiconductor integrated circuit to hold a single bead, on which the DNA strands are immobilized.

Abstract: A biosensor pixel for measuring current that flows through the electrode surface in response to electrochemical interactions and a biosensor array architecture that includes such biosensor pixels. The biosensor pixel includes an electrode transducer configured to measure a current generated by electrochemical interactions occurring at a recognition layer placed directly on top of it in response to an electrical voltage placed across an electrode transducer-electrolyte interface. The biosensor pixel further includes a trans-impedance amplifier connected to the electrode transducer, where the trans-impedance amplifier is configured to convert the current into a voltage signal as the electrochemical interactions occur.

Abstract: This disclosure relates generally to physiological monitoring, and more particularly to feature set optimization for classification of physiological signal. In one embodiment, a method for physiological monitoring includes identifying clean physiological signal training set from an input physiological signal based on a Dynamic Time Warping (DTW) of segments associated with the physiological signal. An optimal features set is extracted from a clean physiological signal training set based on a Maximum Consistency and Maximum Dominance (MCMD) property associated with the optimal feature set that strictly optimizes on the objective function, the conditional likelihood maximization over different selection criteria such that diverse properties of different selection parameters are captured and achieves Pareto-optimality. The input physiological signal is classified into normal signal components and abnormal signal components using the optimal features set.

Abstract: A biosensor pixel for measuring current that flows through the electrode surface in response to electrochemical interactions and a biosensor array architecture that includes such biosensor pixels. The biosensor pixel includes an electrode transducer configured to measure a current generated by electrochemical interactions occurring at a recognition layer placed directly on top of it in response to an electrical voltage placed across an electrode transducer-electrolyte interface. The biosensor pixel further includes a trans-impedance amplifier connected to the electrode transducer, where the trans-impedance amplifier is configured to convert the current into a voltage signal as the electrochemical interactions occur.

Abstract: In many real-life applications, ample amount of examples from one class are present while examples from other classes are rare for training and learning purposes leading to class imbalance problem and misclassification. Methods and systems of the present disclosure facilitate generation of an extended synthetic rare class super dataset that is further pruned to obtain a synthetic rare class dataset by maximizing similarity and diversity in the synthetic rare class dataset while preserving morphological identity with labeled rare class training dataset. Oversampling methods used in the art result in cloning of datasets and do not provide the needed diversity. The methods of the present disclosure can be applied to classification of noisy phonocardiogram (PCG) signals among other applications.

Abstract: A method and device for performing DNA sequencing and extracting structural information from unknown nucleic acid strands. The device includes a microwell structure, where identical DNA strands are immobilized within the microwell structure on a surface of a micro-bead, an active electrode or a porous polymer. The device further includes a CMOS-integrated semiconductor integrated circuit, where the CMOS-integrated semiconductor integrated circuit includes metal layers on a silicon substrate, where the metal layers form an active electrode biosensor. In addition, a sensing electrode is formed by creating openings in a passivation layer of the CMOS-integrated semiconductor integrated circuit to hold a single bead, on which the DNA strands are immobilized.

Abstract: A method and system for a feature subset-classifier pair for a classification task. The classification task corresponds to automatically classifying data associated with a subject(s) or object(s) of interest into an appropriate class based on a feature subset selected among a plurality of features extracted from the data and a classifier selected from a set of classifier types. The method proposed includes simultaneously determining the feature subset-classifier pair based on a relax-greedy {feature subset, classifier} approach utilizing sub-greedy search process based on a patience function, wherein the feature subset-classifier pair provides an optimal combination for more accurate classification. The automatic joint selection is time efficient solution, effectively speeding up the classification task.

Abstract: The present disclosure provides methods, apparatus and systems for time-gated fluorescent-based detection. Time-based fluorescence analysis can be used in certain biochemical assays by measuring the emitted photon flux from fluorophores after an individual excitation pulse.

Abstract: Systems and methods for detecting an anomaly in a cardiovascular signal using hierarchical extremas and repetitions. The traditional systems and methods provide for some anomaly detection in the cardiovascular signal but do not consider the discrete nature and strict rising and falling patterns of the cardiovascular signal and frequency in terms of hierarchical maxima points and minima points. Embodiments of the present disclosure provide for detecting the anomaly in the cardiovascular signal using hierarchical extremas and repetitions by smoothening the cardiovascular signal, deriving sets of hierarchical extremas using window detection, identifying signal patterns based upon the sets of hierarchical extremas, identifying repetitions in the signal patterns based upon occurrences and randomness of occurrences of the signal patterns and classifying the cardiovascular signal as anomalous and non-anomalous for detecting the anomaly in the cardiovascular signal.

Abstract: A method and device for performing DNA sequencing and extracting structural information from unknown nucleic acid strands. The device includes a microwell structure, where identical DNA strands are immobilized within the microwell structure on a surface of a micro-bead, an active electrode or a porous polymer. The device further includes a CMOS-integrated semiconductor integrated circuit, where the CMOS-integrated semiconductor integrated circuit includes metal layers on a silicon substrate, where the metal layers form an active electrode biosensor. In addition, a sensing electrode is formed by creating openings in a passivation layer of the CMOS-integrated semiconductor integrated circuit to hold a single bead, on which the DNA strands are immobilized.

Abstract: The present disclosure addresses the technical problem of information loss while representing a physiological signal in the form of symbols and for recognizing patterns inside the signal. Thus making it difficult to retain or extract any relevant information which can be used to detect anomalies in the signal. A system and method for anomaly detection and discovering pattern in a signal using morphology aware symbolic representation has been provided. The system discovers pattern atoms based on the strictly increasing and strictly decreasing characteristics of the time series physiological signal, and generate symbolic representation in terms of these pattern atoms. Additionally the method possess more generalization capability in terms of granularity. This detects discord/abnormal phenomena with consistency.

Abstract: A method and device for performing DNA sequencing and extracting structural information from unknown nucleic acid strands. The device includes a microwell structure, where identical DNA strands are immobilized within the microwell structure on a surface of a micro-bead, an active electrode or a porous polymer. The device further includes a CMOS-integrated semiconductor integrated circuit, where the CMOS-integrated semiconductor integrated circuit includes metal layers on a silicon substrate, where the metal layers form an active electrode biosensor. In addition, a sensing electrode is formed by creating openings in a passivation layer of the CMOS-integrated semiconductor integrated circuit to hold a single bead, on which the DNA strands are immobilized.

Abstract: The present disclosure provides methods, apparatus and systems for time-gated fluorescent-based detection. Time-based fluorescence analysis can be used in certain biochemical assays by measuring the emitted photon flux from fluorophores after an individual excitation pulse.

Abstract: Current technologies analyze electrocardiogram (ECG) signals for a long duration, which is not always a practical scenario. Moreover the current scenarios perform a binary classification between normal and Atrial Fibrillation (AF) only, whereas there are many abnormal rhythms apart from AF. Conventional systems/methods have their own limitations and may tend to misclassify ECG signals, thereby resulting in an unbalanced multi-label classification problem.

Abstract: Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.

Abstract: A method and device for performing DNA sequencing and extracting structural information from unknown nucleic acid strands. The device includes a microwell structure, where identical DNA strands are immobilized within the microwell structure on a surface of a micro-bead, an active electrode or a porous polymer. The device further includes a CMOS-integrated semiconductor integrated circuit, where the CMOS-integrated semiconductor integrated circuit includes metal layers on a silicon substrate, where the metal layers form an active electrode biosensor. In addition, a sensing electrode is formed by creating openings in a passivation layer of the CMOS-integrated semiconductor integrated circuit to hold a single bead, on which the DNA strands are immobilized.

Abstract: A method and system for a feature subset-classifier pair for a classification task. The classification task corresponds to automatically classifying data associated with a subject(s) or object(s) of interest into an appropriate class based on a feature subset selected among a plurality of features extracted from the data and a classifier selected from a set of classifier types. The method proposed includes simultaneously determining the feature subset-classifier pair based on a relax-greedy {feature subset, classifier} approach utilizing sub-greedy search process based on a patience function, wherein the feature subset-classifier pair provides an optimal combination for more accurate classification. The automatic joint selection is time efficient solution, effectively speeding up the classification task.

Abstract: The present disclosure addresses the technical problem of information loss while representing a physiological signal in the form of symbols and for recognizing patterns inside the signal. Thus making it difficult to retain or extract any relevant information which can be used to detect anomalies in the signal. A system and method for anomaly detection and discovering pattern in a signal using morphology aware symbolic representation has been provided. The system discovers pattern atoms based on the strictly increasing and strictly decreasing characteristics of the time series physiological signal, and generate symbolic representation in terms of these pattern atoms. Additionally the method possess more generalization capability in terms of granularity. This detects discord/abnormal phenomena with consistency.

Abstract: Systems and methods for detecting an anomaly in a cardiovascular signal using hierarchical extremas and repetitions. The traditional systems and methods provide for some anomaly detection in the cardiovascular signal but do not consider the discrete nature and strict rising and falling patterns of the cardiovascular signal and frequency in terms of hierarchical maxima points and minima points. Embodiments of the present disclosure provide for detecting the anomaly in the cardiovascular signal using hierarchical extremas and repetitions by smoothening the cardiovascular signal, deriving sets of hierarchical extremas using window detection, identifying signal patterns based upon the sets of hierarchical extremas, identifying repetitions in the signal patterns based upon occurrences and randomness of occurrences of the signal patterns and classifying the cardiovascular signal as anomalous and non-anomalous for detecting the anomaly in the cardiovascular signal.

Abstract: Current technologies analyze electrocardiogram (ECG) signals for a long duration, which is not always a practical scenario. Moreover the current scenarios perform a binary classification between normal and Atrial Fibrillation (AF) only, whereas there are many abnormal rhythms apart from AF. Conventional systems/methods have their own limitations and may tend to misclassify ECG signals, thereby resulting in an unbalanced multi-label classification problem.