The present disclosure relates to energy efficient battery-operated or self-powered biomedical devices.
Biomedical sensor nodes transmit and possibly process biomedical data gathered by sensors associated with a patient. Biomedical sensor nodes can have a major impact on healthcare if they are capable of providing clinically relevant inferences that are usable for actionable medical decision support. For the purpose of this disclosure an inference is a process of making conclusions using data that is subject to variation that may be caused by observational errors, physiological variances, hardware-induced variances, and/or environmental variations.
Low-power sensing technologies are currently available for continuously providing electrocardiogram (ECG) signals and electroencephalogram (EEG) signals to low power recording devices. However, such signals are subject to numerous physiologic variances that are relatively difficult to model.
Support Vector Machines (SVMs) are state-of-the-art machine-learning classifiers that analyze signal segments by extracting features and representing these features as vectors. Moreover, SVM algorithms are related to supervised learning methods that are rapidly gaining popularity to analyze data, and in particular, biomedical data recovered from complex signals such as ECG and EEG signals as well as relatively less complex signals generated by blood pressure monitors, accelerometers, pulse-oximeters and the like. A classifier model for such signals is generated using selected feature vectors, called support vectors (SVs).
Specialized SVM based biomedical devices have been developed that reduce classification energy usage to a relatively low level using software implemented SVMs. However, such specialized SVM based biomedical devices employ either inflexible SVM algorithms or much lower order models that are insufficient for effectively handling general biomedical signals. Also, while such specialized SVM based biomedical devices can be trained via explicit expert intervention such as through a clinical office visit, they are not flexible enough to efficiently provide clinically relevant inferences needed for remote retraining. As such, existing SVM based biomedical devices are not suitable for comprehensive and adaptive data-driven patient monitoring over a large-scale health care network. For the purpose of this disclosure, a large-scale health care network monitors potentially millions of patients simultaneously. Due to the number of patients involved combined with limited bandwidth and human expert resources, it is not feasible to transmit raw sensor data over a large scale health network for later processing. Therefore, a need remains for a biomedical device that is adaptable to monitor a wide range of biomedical signals while both providing the accuracy of high-order models and using relatively low amounts of classification energy and network resources (bandwidth, personnel, etc.) so that comprehensive and adaptive data-driven patient monitoring over a large-scale health care network is realizable.
The present disclosure provides a biomedical device having a processor that enables relatively advanced inference for sensor nodes while using relatively small amounts of energy during classification operations and model customization/adaptation operations. As a result, the biomedical device of the present application is well suited for providing comprehensive and adaptive data-driven patient monitoring. Physiological signals processable by the present biomedical device include but are not limited to electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, electromyogram (EMG) signals, and electrogastrogram (EGG) signals. Such signals have relatively complex correlations with the clinical states of interest. To make clinically relevant inferences from such signals very advanced computational methods for making the relevant inferences is necessary. The computer science community has developed some very sophisticated computational tools from the domain of machine learning that are applicable to computationally making relevant inferences from physically complex signals. However, the computer science community has directed their efforts towards machine learning theoretical constructs as opposed to being concerned with computational energy requirements, network/communication limitations, clinical resource limitations, and constraints of a battery operated biomedical monitoring device.
In general, the biomedical device of the present disclosure includes a receiver to receive sensor data associated with physiological signals and perform feature computations on the sensor data, representing data instances. In at least one embodiment of the present disclosure this is done in a highly programmable way, accommodating a wide range of signals and applications. The biomedical device further includes a control system to classify the sensor data using the feature computations to generate relevant decisions, including automatic selection of a set of relevant data instances. The control system further provides medically-relevant decisions by analyzing received sensor data associated with physiological signals. The medically-relevant decisions are improved through an adaptive process described in greater detail below.
The biomedical device is also adapted to receive a patient-generic seed model that is usable by the control system to automatically select a coarse set of more relevant data instances. A wireless interface included with the biomedical device is adapted to wirelessly transmit the coarse set of more relevant data instances to the base station over a wide area network (WAN). The base station analyzes the coarse set of more relevant data instances to generate a patient-specific model. The biomedical device is further adapted to receive from the base station the patient-specific model that is usable by the control system to automatically select a refined set of more relevant data instances. The wireless interface integrated with the biomedical device is adapted to wirelessly transmit the refined set of more relevant data instances to the base station over the WAN.
One objective of the present disclosure is to break down and reconfigure software components of machine learning tools into hardware blocks that have broad applicability for gathering relevant inferences from a broad range of biomedical signal types. Another objective is to adapt the hardware blocks to consume amounts of energy that are orders of magnitude lower relative to traditional software operations performing equivalent tasks. Yet another objective of at least one embodiment is to integrate the hardware blocks with a programmable processor thereby making up a System-on-Chip (SOC) device that offers a high degree of programmability, for instance, for feature computations. Preferably, the SOC device further includes a radio frequency (RF) transceiver for communicating data and commands to and from the SOC device. In at least one embodiment a Bluetooth transceiver is suitable as the RF transceiver of the SOC. Most modern mobile terminals such as a cellular handset, a personal digital assistant, smart phone, or the like include Bluetooth capability. Therefore, the SOC device combined with a mobile terminal communication link can communicate with a WAN such as a large-scale health network to report reduced patient data sets to a remote clinical workstation, etc.
Those skilled in the art will appreciate the scope of the present disclosure and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures.
The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.
The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims.
A clock (CLK) signal for synchronizing the operations of the biomedical device 10 is provided via a CLK input 28. A timer clock signal for watchdog timing and/or real-time date stamping is provided to the biomedical device 10 via a TIMER_CLK input 30. Moreover, a reset (RST) signal for resetting the biomedical device 10 is provided via a RST input 32.
The biomedical device 10 further includes a first memory management unit (MMU1) 34 for managing memory access to a first external memory (MEM1) 36. The MEM136 is usable to store raw sensor data provided by the sensor module 26. Preferably the MEM136 can store at least one megabyte of data. The CPU core 12 can access the data stored in the MEM136 via a peripheral interface bus 38 and the MMU134.
A wireless interface 40 communicates with a wireless device 42 in order to pass data and commands between the biomedical device 10 and a wireless appliance (not shown) such as a smart phone, which in turn passes data and commands between the biomedical device 10 and a large-scale health network via the wireless appliance. The large-scale health network is typically in communication with the Internet which includes cellular gateways such as fourth generation (4G) cellular networks. The wireless interface 40 includes a radio interface (I/F) block 44, a buffer 46, and a universal asynchronous receiver transmitter (UART) 48. It is preferable for the buffer 46 to have at least eight kilobytes of memory.
The wireless interface 40 communicates with the CPU core 12 over the peripheral interface bus 38. The wireless device 42 is shown as an external Bluetooth transceiver that has a relatively short range and energy efficient radio protocol. It is to be understood that the wireless device 42 could be integrated into the biomedical device 10 as a wireless transceiver.
The biomedical device 10 further includes a data-driven classifier such as a support vector machine (SVM) 50 that is implemented in hardware and adapted to automatically classify feature vectors (FVs) generated from raw sensor data collected by sensor module 26. The SVM 50 implements SVM kernels in hardware rather than using traditional software algorithms to increase computational speed. Another highly beneficial result of implementing the SVM 50 rather than implementing a traditional software-based SVM is a relatively large reduction in classification energy usage. For example, the SVM 50 reduces classification energy usage within a range of 40 to 100 times over traditional software-based SVMs when processing relatively large support vector (SV) models having thousands to hundreds of thousands of SVs.
The biomedical device 10 also includes an SVM accelerator 52 that is implemented in hardware to efficiently perform some of the computations required by the SVM 50. The SVM accelerator 52 provides flexible partitioning of an SV memory 54, which enables multiple SVM instances for multi-class detectors, classification boosting, and alternate kernel functions that are usable for model adaptation. A second external memory (MEM2) 58 is usable as extended SV memory if more memory than the memory allotted for SV memory 54 is exceeded. The MEM258 is depicted as having about one megabyte of memory, but it is to be understood that larger or smaller quantities of memory are usable as MEM258. The SVM accelerator 52 is preferably incorporated in the SVM 50 such that hardware comprising the SVM accelerator 52 is shared with the ALDS unit 64.
The SVM accelerator 52 also includes a second memory management unit (MMU2) 56 for managing data access between the CPU core 12 via the peripheral interface bus 38 and the SV memory 54. The MMU256 also manages data access between the MEM258 and the CPU core 12 via the peripheral interface bus 38.
The biomedical device 10 is reconfigurable such that conventional SVM computations can be restructured for a broad range of biomedical monitoring applications. The reconfigurability provided by the biomedical device 10 further significantly reduces classification energy usage while processing relatively large SV models.
A coordinate rotation digital computer (CORDIC) 60 integrated in the SVM accelerator 52 is usable to implement a non-linear transformation function (K) that enhances the flexibility of a classifier processed by the SVM 50. The non-linear transformation function K is selectable for energy scalability across applications.
A multiply and accumulate (MAC) 62 is usable to perform in-line scaling to apply SVM model parameters. The MAC 62 also performs configurable shifting to truncate summations performed over various model sizes.
Patient-specific data-driven modeling implemented by the biomedical device 10 uses physiological signals from a particular patient to form customized SV models. As a result, the biomedical device 10 significantly improves accuracy across a broad range of clinical applications that rely on analyzing complex signals such as electrocardiogram (ECG) signals and electroencephalogram (EEG) signals.
An adaptive learning data selection (ALDS) unit 64 that enables automated learning is integrated with the biomedical device 10 for selecting a relatively highly reduced set of FVs that contain data that is automatically prescreened to yield medically relevant data for a health expert such as a clinician. The automatic prescreening of data via the ALDS unit 64 reduces the raw sensor data by potentially a factor of several thousand. As a result, only a relatively small fraction of bandwidth is needed to transmit the highly reduced data set via the wireless device 42 to a wireless appliance (not shown), which in turn makes the reduced data set available to authorized personnel over a large-scale health network. Also, the automatic prescreening of data via the ALDS unit 64 significantly reduces burdens placed on clinicians that receive the data in that they will not have to search through a raw data set for significant medically relevant features because the automatic prescreening of the data will have already located the most significant relevant features. The ALDS unit 64 is implemented in hardware rather than software to increase computational speed, increase energy efficiency, and alleviate computational burden from the CPU. Moreover, it is preferred that the CPU core 12, the SVM 50, the ALDS unit 64, and the SVM accelerator 52 are integrated to form a system-on-chip (SOC) device.
Preferably, a power management unit (PMU) 66 is integrated into the biomedical device 10 for providing idle-mode clock-gating and/or power-gating control of the MMU134, the radio I/F 44, the UART 48, the SVM 50, the MMU256, the CORDIC 60, the MAC 62, and the ALDS unit 64. The idle-mode control used for the biomedical device 10 provides idle synchronization for the blocks having an idle mode. In this way, energy usage is minimized when no hardware implemented processing is occurring.
The MAC 62 can employ various embodiments of logic structures to support computations for the SVM 50 and the ALDS unit 64. In the following exemplary embodiment, the hardware of the MAC 62 is made up of a first operand multiplexer 72 and a second operand multiplexer 74. A subtract (SUB) register 76 receives output from the first operand multiplexer 72 and the second operand multiplexer 74. A third operand multiplexer 78 receives output from the first operand multiplexer 72 and the SUB register 76 while a fourth operand multiplexer 80 receives output from the second operand multiplexer 74 and the SUB register 76. Both the first operand multiplexer 72 and the second operand multiplexer 74 are preferably thirty-two bits (32 b) wide.
A multiply (MULT) register 82 receives outputs from both the third operand multiplexer 78 and the fourth operand multiplexer 80. The MULT register 82 is preferably sixty-four bits (64 b) wide.
A first results register 84 receives output from the MULT register 82. A fifth operand multiplexer 86 and a sixth operand multiplexer 88 receive outputs from the first results register 84. The fifth operand multiplexer 86 and the sixth operand multiplexer 88 also receive inputs of constants that together with the contents of the first results register 84 are processed by an add/subtract (ADD/SUB) register 90.
A seventh operand multiplexer 92 and an eighth operand multiplexer 94 receive the processed contents of the ADD/SUB register 90. A SHIFT register 96 receives output from the seventh operand multiplexer 92 and is responsive to a shift signal. A second results register 98 receives the output of the SHIFT register 96 as a SV dot product.
A final results register 100 receives output from the eighth operand multiplexer 94 and stores the output as final results of the MAC 62. While the MULT register 82 and the ADD/SUB register 90 in the particular embodiment shown in
In operation, the ALDS unit 64 (
A process flow for the ALDS unit 64 begins with an IDLE process that waits for a data instance (step 200). The data instance is used to get a marginal distance (d) via the SVM 50 (step 202). A diversity value (c) is computed via the CORDIC 60 and the MAC 62 (step 204). Once the marginal distance (d) and the diversity value (c) are computed, a score function [λ1d+λ2c] is derived (step 206). The values λ1 and λ2 are weights that are assigned to the score function [λ1d+λ2c] by the ALDS unit 64. The ALDS unit 64 uses the score function [λ1d+λ2c] to select data that has a minimum score (step 208). The selection of data with a minimum score continues as the ALDS unit 64 iterates through a pool of SVs until a batch of data is computed (Step 210).
The ALDS unit 64 performs on-going data batch selection until a terminate block 108 determines that enough batch data has been collected to construct and update a new SV model. The wireless interface 40 transmits data batches and receives updated SV models by communicating with a base station 110 on which model construction is realized.
The exemplary application summary portion of the table of
Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.
This invention was made with government funds under contract number HR0011-07-3-0002 awarded by DARPA. The U.S. Government has rights in this invention.