This paper presents a multi-channel biomedical sensor system with system-level chopping and stochastic analog-to-digital (A/D) conversion techniques. The system-level chopping technique extends the input-signal bandwidth and reduces the interchannel crosstalk caused by multiplexing. The system-level chopping can replace an analog low-pass filter (LPF) with a digital filter and can reduce its area occupation. The stochastic A/D conversion technique realizes power-efficient resolution enhancement. A novel auto-calibration technique is also proposed for the stochastic A/D conversion technique. The proposed system includes a prototype analog front-end (AFE) IC fabricated using a 130 nm CMOS process. The fabricated AFE IC improved its interchannel crosstalk by 40 dB compared with the conventional analog chopping architecture. The AFE IC achieved SNDR of 62.9 dB at a sampling rate of 31.25 kSps while consuming 9.6 μW from a 1.2 V power supply. The proposed resolution enhancement technique improved the measured SNDR by 4.5 dB.

An output voltage estimation and regulation system for a wireless power transfer (WPT) circuit is proposed. Since the fluctuation of a coupling condition and/or a load may vary the voltage supplied with WPT resulting in a malfunction of wireless-powered devices, the output voltage regulation is needed. If the output voltage is regulated by a voltage regulator in a secondary side of the WPT circuit with fixed input power, the voltage regulator wastes the power to regulate the voltage. Therefore the output voltage regulation using a primary-side control, which adjusts the input power depending on the load and/or the coupling condition, is a promising approach for efficient regulation. In addition, it is desirable to eliminate feedback loop from the secondary side to the primary side from the viewpoint of reducing power dissipation and system complexity. The proposed system can estimate and regulate the output voltage independent of both the coupling and the load variation without the feedback loop. An usable range of the coupling coefficient and the load is improved compared to previous works. The validity of the proposed system is confirmed by the SPICE simulator.

Pain is an essential physiological phenomenon of human beings. Accurate assessment of pain is important to develop proper treatment. Although self-report method is the gold standard in pain assessment, it is not applicable to individuals with communicative impairment. Non-verbal pain indicators such as pain related facial expressions and changes in physiological parameters could provide valuable insights for pain assessment. In this paper, we propose a multimodal-based Stream Integrated Neural Network with Different Frame Rates (SINN) that combines facial expression and biomedical signals for automatic pain assessment. The main contributions of this research are threefold. (1) There are four-stream inputs of the SINN for facial expression feature extraction. The variant facial features are integrated with biomedical features, and the joint features are utilized for pain assessment. (2) The dynamic facial features are learned in both implicit and explicit manners to better represent the facial changes that occur during pain experience. (3) Multiple modalities are utilized to identify various pain states, including facial expression and biomedical signals. The experiments are conducted on publicly available pain datasets, and the performance is compared with several deep learning models. The experimental results illustrate the superiority of the proposed model, and it achieves the highest accuracy of 68.2%, which is up to 5% higher than the basic deep learning models on pain assessment with binary classification.

This paper presents a numerical study of the wireless channel characteristics of liver implants in a frequency range of 4.5-6.5GHz, considering different digital human phantoms by employing two inhomogeneous male and female models. Path loss data for in-body to on-body and in-body to off-body communication scenarios are provided. The influence of respiration-induced organ movement on signal attenuation is demonstrated. A narrower range of attenuation deviation is observed in the female model as compared to the male model. The path loss data in the female body is between 40-80dB which is around 5-10dB lower than the male model. Path loss data for the in-body to off-body scenario in both models suggest that in-body propagation is the main component of total path loss in the channel. The results demonstrate that channel characteristics are subject dependent, and thus indicate the need to take subject dependencies into consideration when investigating in-body communication channels.

A phase locking value (PLV) in electrocorticography is an essential indicator for analysis of cognitive activities and detection of severe diseases such as seizure of epilepsy. The PLV computation requires a simultaneous pursuit of high-throughput and low-cost implementation in hardware acceleration. The PLV computation consists of bandpass filtering, Hilbert transform, and mean phase coherence (MPC) calculation. The MPC calculation includes trigonometric functions and divisions, and these calculations require a lot of computational amounts. This paper proposes an MPC calculation method that removes high-cost operations from the original MPC with mathematically identical derivations while the conventional methods sacrifice either computational accuracy or throughput. This paper also proposes a hardware implementation of MPC calculator whose latency is 21 cycles and pipeline interval is five cycles. Compared with the conventional implementation with the same standard cell library, the proposed implementation marks 2.8 times better hardware implementation efficiency that is defined as throughput per gate counts.

Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.

This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.

We review recently obtained results for CMOS (Complementary Metal Oxide Semiconductor) imaging devices used in biomedical applications. The topics include dish type image sensors, deep-brain implantation devices for small animals, and retinal prosthesis devices. Fundamental device structures and their characteristics are described, and the results of in vivo experiments are presented.

The objective of this study is to investigate the feasibility of an ultra wideband (UWB) impulse radio system for in-body to off-body wireless communication for biomedical applications. At first, a UWB antenna is designed in the UWB low band for implant use in the chest. Then the channel model is extracted and established based on the finite difference time domain (FDTD) simulation with an anatomical human body model. The established channel model consists of a small set of parameters for generating discrete time impulse responses. The generated model shows good agreement with the FDTD-calculated result in terms of key communication metrics. For effective communication over the multipath-affected channel, the pulse position modulation is employed and a 2-finger RAKE structure with a constant temporal delay is proposed in the receiver. The bit error rate performance has shown the validity of the system in the in-body to off-body chest channel.

This work elucidates the evolution of three generations of the laboratory information system in the National Taiwan University Hospital, which were respectively implemented in an IBM Series/1 minicomputer, a client/server and a plug-and-play HL7 interface engine environment respectively. The experience of using the HL7 healthcare information exchange in the hospital information system, laboratory information system, and automatic medical instruments over the past two decades are illustrated and discussed. The latest design challenge in developing intelligent laboratory information services is to organize effectively distributed and heterogeneous medical instruments through the message gateways. Such experiences had spread to some governmental information systems for different purposes in Taiwan; besides, the healthcare information exchange standard, software reuse mechanism, and application service provider adopted in developing the plug-and-play laboratory information system are also illustrated.

In this paper, we propose new external context features for the semantic classification of bio-entities. In the previous approaches, the words located on the left or the right context of bio-entities are frequently used as the external context features. However, in our prior experiments, the external contexts in a flat representation did not improve the performance. In this study, we incorporate predicate-argument features into training the ME-based classifier. Through parsing and argument identification, we recognize biomedical verbs that have argument relations with the constituents including a bio-entity, and then use the predicate-argument structures as the external context features. The extraction of predicate-argument features can be done by performing two identification tasks: the biomedically salient word identification which determines whether a word is a biomedically salient word or not, and the target verb identification which identifies biomedical verbs that have argument relations with the constituents including a bio-entity. Experiments show that the performance of semantic classification in the bio domain can be improved by utilizing such predicate-argument features.

In this paper, we propose two weighting techniques to improve performances of query expansion in biomedical document retrieval, especially when a short biomedical term in a query is expanded with its synonymous multi-word terms. When a query contains synonymous terms of different lengths, a traditional IR model highly ranks a document containing a longer terminology because a longer terminology has more chance to be matched with a query. However, such preference is clearly inappropriate and it often yields an unsatisfactory result. To alleviate the bias weighting problem, we devise a method of normalizing the weights of query terms in a long multi-word biomedical term, and a method of discriminating terms by using inverse terminology frequency which is a novel statistics estimated in a query domain. The experiment results on MEDLINE corpus show that our two simple techniques improve the retrieval performance by adjusting the inadequate preference for long multi-word terminologies in an expanded query.

An experimental study concerning Ultra Wideband (UWB) Microwave Radar for breast cancer detection is described. A simple phantom, consisting of a cylindrical plastic container with a low dielectric constant material imitating fatty tissues and a high dielectric constant object emulating tumour, is scanned with a tapered slot antenna operating between 3.1 to 10.6 GHz. A successful detection of a target is accomplished by a visual inspection of a two-dimensional image of the scanned phantom

In this paper, an ultra-low power variable-resolution sigma-delta (ΣΔ) modulator for biomedical application is presented. The resolution of proposed modulator can be adjusted by switching its sampling frequency and architecture. The architecture is switched between second-order single-loop modulator and fourth-order cascaded second stage noise shaped modulator to reach different resolution requirement. The proposed sigma-delta modulator is implemented by single phase integrators based on a fully differential switched-capacitor circuit. The digital cancellation logic is embedded in the chip so that it would easily be integrated with biomedical instrument for effective acquisition. Experimental results of the proposed variable-resolution ΣΔ modulator fabricated in standard CMOS 0.18 µm technology confirm the expected specifications from 65 dB signal-to-noise distortion to 96 dB with 1 kHz bandwidth and power consumption range from 48 µW to 360 µW with a 1.8 V battery supply.

This paper presents a new model which computes the deformation and the feedback force of high-resolution biomedical volumetric objects consisting of hundreds of thousands of volume elements. The main difficulty in the simulation of these high-resolution volumetric objects is to compute and generate stable feedback force from the objects within a haptic update time (1 msec). In our model, springs are used in order to represent material properties of volume elements and cylinders are used to activate corresponding springs according to the amount of deformation. Unlike in a mass-spring model, springs in our model have constraint conditions. In our model, the deformation is calculated locally and then is propagated outward through object's volume as if a chain is pulled or pushed. The deformed configuration is then used to compute the object's internal potential energy that is reflected to the user. The simple nature of our model allows the much faster calculation of the deformation and the feedback force from the volumetric deformable object than the conventional model (an FEM or a mass-spring model). Experiments are conducted with homogenous and non-homogenous volumetric cubic objects and a volumetric human liver model obtained from CT data at a haptic update rate of 1000 Hz and a graphic update rate of 100 Hz to show that our model can be utilized in the real-time volume haptic rendering. We verify that our model provides a realistic haptic feeling for the user in real time through comparative study.

Subcircuits designed for integrated silicon DCS1800/ PCS1900 base station receivers have been reconfigured into hybrid and single-chip Doppler radar transceivers. Radar chips have been fully integrated in 0.25 µm silicon CMOS and BiCMOS processes. These chips have been used to monitor heart and respiration activity without contact, and they have successfully detected heartbeat and respiration rate up to 1 m from the subject. This monitoring device may be useful in home monitoring, continuous monitoring, and physiological research.