Clinical utility of anal sphincter relaxation integral in water-perfused and solid-state high-resolution anorectal manometry.

BACKGROUND/PURPOSE: We investigated the diagnostic performance of the anal sphincter relaxation integral (ASRI) for infants with Hirschsprung's disease (HD). METHODS: We performed water-perfused high-resolution anorectal manometry (HRAM) in 18 infants (9 with HD), and solid-state HRAM in another 18 infants (4 with HD). We calculated the ASRI during the rectoanal inhibitory reflex (RAIR) maneuver at pressure cutoffs of <10 mmHg (ASRI 10) and <15 mmHg (ASRI 15). We investigated the diagnostic performance of the ASRI for HD in infants undergoing water-perfused and solid-state HRAM. RESULTS: HD infants who underwent either water-perfused or solid-state HRAM had significantly lower ASRI 10 and ASRI 15 values, compared with non-HD infants (P < 0.05 and P < 0.05, respectively). Using the water-perfused HRAM system, ASRI 10 and ASRI 15 values of <7 and <29 mmHg s.cm, respectively, exhibited good diagnostic performance for HD (88.89% and 88.89%, respectively). Receiver operating characteristic curve analysis indicated that ASRI 10 and ASRI 15 values of <5.5 and <20 mmHg s.cm, respectively, were optimal for the diagnosis of HD infants when using the solid-state HRAM system, with high diagnostic accuracies of 83.33% and 83.33%, respectively. CONCLUSION: ASRI may assist the diagnosis of HD infants using either water-perfused or solid-state HRAM. These systems require different catheter-specific ASRI cutoffs for the prediction of HD.

Pressure-impedance analysis: Assist the diagnosis and classification of ineffective esophageal motility disorder.

BACKGROUND AND AIM: We elucidated the clinical significance of distal contractile integral-to-esophageal impedance integral (EII) ratio (DCIIR) in ineffective esophageal motility (IEM) adult patients. METHODS: We recruited 101 patients with IEM (48.38 ± 1.58 years) and 42 matched healthy volunteers (44.28 ± 1.85 years) in this case-control study. All subjects underwent esophageal high-resolution impedance manometry from October 2014 to May 2018. The diagnosis of IEM was based on the Chicago Classification version 3.0. The EII, EII ratio, and DCIIR were analyzed by matlab software. RESULTS: The EII, EII ratio, and DCIIR calculated at an impedance threshold of 1500 Ω (EII1500, EII ratio1500, and DCIIR1500, respectively) were significantly lower in the IEM group than in healthy controls (P < 0.0001, < 0.0001, and < 0.0001, respectively). Receiver operating characteristic analysis showed that DCIIR1500 < 0.008 mmHg/Ω, EII1500 > 71 000 Ω.s.cm, and EII ratio1500 > 0.43 were all predictive of IEM. Only DCIIR1500 < 0.008 mmHg/Ω remained significant in diagnosing IEM in the multivariate logistic regression analysis (odds ratio = 72.13, P < 0.001). The DCIIR1500 is negatively correlated with Eckardt score and the Reflux Disease Questionnaire (correlation coefficient = -0.2844 and -0.3136; P = 0.0006 and 0.0002, respectively). Receiver operating characteristic analysis further showed that a DCIIR1500 cut-off of 0.002 mmHg/Ω achieved the best differentiation between the IEM-alternans and IEM-persistens subtypes among IEM patients (P < 0.001). CONCLUSIONS: The novel pressure-impedance parameter of high-resolution impedance manometry, DCIIR1500, may assist in the diagnosis and classification of IEM and correlated with clinical symptoms.

Diagnostic Role of Anal Sphincter Relaxation Integral in High-Resolution Anorectal Manometry for Hirschsprung Disease in Infants.

OBJECTIVE: To investigate the possible diagnostic role of anal sphincter relaxation integral (ASRI) in high-resolution anorectal manometry (HRAM) for Hirschsprung disease. STUDY DESIGN: We performed conventional anorectal manometry (ARM) in 24 infants (8 with Hirschsprung disease and 16 without Hirschsprung disease) and HRAM in another 21 infants (9 with Hirschsprung disease and 12 without Hirschsprung disease) before and after October 2014. All infants underwent rectal suction biopsy for confirmation of Hirschsprung disease. We quantified rectoanal inhibitory reflex (RAIR) adequacy by calculating the ASRI in HRAM study at pressure cutoffs of less than 10, 15, and 20 mm Hg (ASRI10, ASRI15, and ASRI20, respectively) and investigated the diagnostic utility. RESULTS: Patients with Hirschsprung disease who underwent HRAM had significantly lower ASRI10, ASRI15, and ASRI20 values than did infants without Hirschsprung disease (P = .0002, .0002, and .0003, respectively), indicating significant difference in internal anal sphincter relaxation during RAIR test between these 2 groups. ASRI10 exhibited a greater diagnostic accuracy, area under the curve, sensitivity, and specificity than did ASRI15 and ASRI20 for Hirschsprung disease. Moreover, the diagnostic accuracy of HRAM for Hirschsprung disease based on ASRI10 <7 mm Hg.s.cm was significantly greater than that of conventional ARM (P = .02). CONCLUSIONS: ASRI10 may be indicative of the adequacy of RAIR by HRAM in infants, thus assisting the diagnosis of Hirschsprung disease. The diagnostic accuracy of HRAM (based on the ASRI10 value) is greater than that of conventional ARM for Hirschsprung disease. ASRI10 may be used in an automatic HRAM analysis system for the diagnosis of anorectal motility disorders.

Distal contractile to impedance integral ratio assist the diagnosis of pediatric ineffective esophageal motility disorder.

OBJECTIVES: We investigated the diagnostic utility of distal contractile integral (DCI) to esophageal impedance integral (EII) ratio (DCIIR) in high-resolution impedance manometry (HRIM) of ineffective esophageal motility (IEM) in children. METHODS: We performed HRIM in 31 children with dysphagia, odynophagia, chronic vomiting, chest pain, or heartburn sensation. Based on the Chicago classification version 3.0, 20 subjects were diagnosed with IEM, and 11 subjects were normal. We analyzed the EII and DCIIR using MATLAB software. RESULTS: The DCIIR calculated at the impedance cutoff at 1500 Ω (DCIIR1500) were significantly lower in IEM group than patients with normal motility (P = 0.007). Receiver operating characteristic (ROC) curve analysis showed that a DCIIR1500 < 0.009 mmHg/Ω best predicted IEM in children (P < 0.001). A DCIIR1500 < 0.008 mmHg/Ω is associated with significant body weight loss > 10% within 6 months in children. (P < 0.001). CONCLUSIONS: The calculation of DCIIR1500 may assist the automatic analysis of bolus transit in HRIM study to diagnose IEM in children. An DCIIR1500 < 0.009 mmHg/Ω may assist in the diagnosis of IEM in children, and DCIIR1500 < 0.008 mmHg/Ω correlated with significant body weight loss. The calculation of DCIIR may serve as possible parameters for HRIM.

sBWT: memory efficient implementation of the hardware-acceleration-friendly Schindler transform for the fast biological sequence mapping.

MOTIVATION: The Full-text index in Minute space (FM-index) derived from the Burrows-Wheeler transform (BWT) is broadly used for fast string matching in large genomes or a huge set of sequencing reads. Several graphic processing unit (GPU) accelerated aligners based on the FM-index have been proposed recently; however, the construction of the index is still handled by central processing unit (CPU), only parallelized in data level (e.g. by performing blockwise suffix sorting in GPU), or not scalable for large genomes. RESULTS: To fulfill the need for a more practical, hardware-parallelizable indexing and matching approach, we herein propose sBWT based on a BWT variant (i.e. Schindler transform) that can be built with highly simplified hardware-acceleration-friendly algorithms and still suffices accurate and fast string matching in repetitive references. In our tests, the implementation achieves significant speedups in indexing and searching compared with other BWT-based tools and can be applied to a variety of domains. AVAILABILITY AND IMPLEMENTATION: sBWT is implemented in C ++ with CPU-only and GPU-accelerated versions. sBWT is open-source software and is available at http://jhhung.github.io/sBWT/Supplementary information: Supplementary data are available at Bioinformatics online. CONTACT: chyee@ntu.edu.tw or jhhung@nctu.edu.tw (also juihunghung@gmail.com).

An FM-Index Based High-Throughput Memory-Efficient FPGA Accelerator for Paired-End Short-Read Mapping.

This article presents an Ferragina-Manzini index (FM-index) based paired-end short-read mapping hardware accelerator. Four techniques are proposed to significantly reduce the number of memory accesses and operations to improve the throughput. First, an interleaved data structure is proposed to reduce the processing time by 51.8% by leveraging the data locality. Second, the boundaries of possible mapping location candidates can be retrieved within only one memory access by constructing a lookup table along with the FM-index. This reduces the number of DRAM accesses by 60% with only a 64 MB memory overhead. Third, an additional step is added to skip the time-consuming repetitive location candidates filtering conditionally, avoiding unnecessary operations. Lastly, an early termination method is proposed to terminate the mapping process if any location candidate with a high enough alignment score is detected, greatly decreasing the execution time. Overall, the computation time is reduced by 92.6% with only a 2% memory overhead in DRAM. The proposed methods are realized on a Xilinx Alveo U250 FPGA. The proposed FPGA accelerator processes 1,085,812,766 short-reads from the U.S. Food and Drug Administration (FDA) dataset within 35.4 minutes at 200 MHz. It achieves a 1.7-to-18.6× higher throughput and the highest 99.3% accuracy by exploiting the paired-end short-read mapping, compared to state-of-the-art FPGA-based designs.

Achieving Accurate Automatic Sleep Apnea/Hypopnea Syndrome Assessment Using Nasal Pressure Signal.

Automatic assessment of sleep apnea/ hypopnea syndrome (SAHS) based on fewer physiological signals is critical for the success of healthcare at home. However, previous studies that use such settings only achieve a lower assessment accuracy, causing fewer syndromes to be separated for effective diagnosis. This paper presents a 3-stage support vector machines (SVM)-based algorithm for SAHS assessment using a single-channel nasal pressure (NP) signal. In this work, NP signal is utilized for feature extraction. Amplitude features, as well as those extracted using discrete Fourier transform and discrete wavelet transform, are used for machine learning. A total of 58 sets of polysomnography recordings, each with approximately 7 h in duration, were analyzed. This work achieves a sensitivity of 95.7% and a positive predictive value of 90.9%, outperforming previous works using NP signal. Compared with prior studies using only SpO2 signal, this work still achieves better performance and supports more classification levels. Thanks to the low-complexity settings based only on the NP signal, the proposed approach provides a promising solution to SAHS assessment for remote healthcare.

Bolus transit of upper esophageal sphincter on high-resolution impedance manometry study correlate with the laryngopharyngeal reflux symptoms.

Laryngopharyngeal reflux symptom is a troublesome upper esophageal problem, and reflux symptom index (RSI) is commonly applied for the assessment of clinical severity. We investigated the relationship between the upper esophageal sphincter impedance integral (UESII) and RSI scores in this study. Totally 158 subjects with high-resolution esophageal impedance manometry (HRIM) with RSI questionnaire assessment were recruited. There are 57 (36.08%), 74 (46.84%), 21 (13.29%), and 6 (3.79%) patients were categorized as normal, ineffective esophageal motility disorder, absent contractility, and achalasia by HRIM examination, respectively. Subjects with RSI > 13 were noted to have lower UESII than others with RSI ≦ 13 (7363.14 ± 1085.58 vs. 11,833.75 ± 918.77 Ω s cm; P < 0.005). The ROC analysis yielded a UESII cutoff of < 2900 Ω s cm for the best prediction of subjects with RSI > 13 (P = 0.002). Both female gender and UESII cutoff of < 2900 Ω s cm were significant predictors of RSI > 13 in logistic regression analysis (OR = 3.84 and 2.83; P = 0.001 and 0.01; respectively). Lower UESII on HRIM study, indicating poor bolus transit of UES during saline swallows, is significantly associated with prominent laryngopharyngeal reflux symptoms scored by RSI score.

Design and In Vivo Verification of a CMOS Bone-Guided Cochlear Implant Microsystem.

OBJECTIVE: To develop and verify a CMOS bone-guided cochlear implant (BGCI) microsystem with electrodes placed on the bone surface of the cochlea and the outside of round window for treating high-frequency hearing loss. METHODS: The BGCI microsystem consists of an external unit and an implanted unit. The external system-on-chip is designed to process acoustic signals through an acquisition circuit and an acoustic DSP processor to generate stimulation patterns and commands that are transmitted to the implanted unit through a 13.56 MHz wireless power and bidirectional data telemetry. In the wireless power telemetry, a voltage doubler/tripler (2X/3X) active rectifier is used to enhance the power conversion efficiency and generate 2 and 3 V output voltages. In the wireless data telemetry, phase-locked loop based binary phase-shift keying and load-shift keying modulators/demodulators are adopted for the downlink and uplink data through high-Q coils, respectively. The implanted chip with four-channel high-voltage-tolerant stimulator generates biphasic stimulation currents up to 800 µA. RESULTS: Electrical tests on the fabricated BGCI microsystem have been performed to verify the chip functions. The in vivo animal tests in guinea pigs have shown the evoked third wave of electrically evoked auditory brainstem response waveforms. It is verified that auditory nerves can be successfully stimulated and acoustic hearing can be partially preserved. CONCLUSION AND SIGNIFICANCE: Different from traditional cochlear implants, the proposed BGCI microsystem is less invasive, preserves partially acoustic hearing, and provides an effective alternative for treating high-frequency hearing loss.

A 135-mW Fully Integrated Data Processor for Next-Generation Sequencing.

Next-generation sequencing (NGS) enables high-throughput sequencing, in which short DNA fragments can be sequenced in a massively parallel fashion. However, the essential algorithm behind the succeeding NGS data analysis, DNA mapping, is still excessively time consuming. DNA mapping can be partitioned into two parts: suffix array (SA) sorting and backward searching. Dedicated hardware designs for the less-complex backward searching have been proposed, but feasible hardware for the most complicated part, SA sorting, has never been explored. Based on the memory-efficient sBWT algorithm, this work is the first integrated NGS data processor for the entire DNA mapping. The -ordered Ferragina and Manzini index used in the sBWT algorithm is leveraged to improve storage capacity and reduce hardware complexity. The proposed NGS data processor realizes the sBWT algorithm through bucket sorting, suffix grouping, and suffix sorting circuits. Key design parameters are analyzed to achieve the optimal performance with respect to hardware cost and execution time. Fabricated in 40-nm CMOS, the NGS data processor dissipates 135 mW at 200 MHz from a 0.9-V supply. With 1-GB external memory, the chip can analyze human DNA within 10 min. This work achieves 43 065 and 8 971 [3208 and 402 ] higher energy efficiency (throughput-to-area ratio) than the high-end CPU and GPU solutions, respectively.

An 81.6 µW FastICA processor for epileptic seizure detection.

To improve the performance of epileptic seizure detection, independent component analysis (ICA) is applied to multi-channel signals to separate artifacts and signals of interest. FastICA is an efficient algorithm to compute ICA. To reduce the energy dissipation, eigenvalue decomposition (EVD) is utilized in the preprocessing stage to reduce the convergence time of iterative calculation of ICA components. EVD is computed efficiently through an array structure of processing elements running in parallel. Area-efficient EVD architecture is realized by leveraging the approximate Jacobi algorithm, leading to a 77.2% area reduction. By choosing proper memory element and reduced wordlength, the power and area of storage memory are reduced by 95.6% and 51.7%, respectively. The chip area is minimized through fixed-point implementation and architectural transformations. Given a latency constraint of 0.1 s, an 86.5% area reduction is achieved compared to the direct-mapped architecture. Fabricated in 90 nm CMOS, the core area of the chip is 0.40 mm(2). The FastICA processor, part of an integrated epileptic control SoC, dissipates 81.6 µW at 0.32 V. The computation delay of a frame of 256 samples for 8 channels is 84.2 ms. Compared to prior work, 0.5% power dissipation, 26.7% silicon area, and 3.4 × computation speedup are achieved. The performance of the chip was verified by human dataset.

A fully integrated nose-on-a-chip for rapid diagnosis of ventilator-associated pneumonia.

Ventilator-associated pneumonia (VAP) still lacks a rapid diagnostic strategy. This study proposes installing a nose-on-a-chip at the proximal end of an expiratory circuit of a ventilator to monitor and to detect metabolite of pneumonia in the early stage. The nose-on-a-chip was designed and fabricated in a 90-nm 1P9M CMOS technology in order to downsize the gas detection system. The chip has eight on-chip sensors, an adaptive interface, a successive approximation analog-to-digital converter (SAR ADC), a learning kernel of continuous restricted Boltzmann machine (CRBM), and a RISC-core with low-voltage SRAM. The functionality of VAP identification was verified using clinical data. In total, 76 samples infected with pneumonia (19 Klebsiella, 25 Pseudomonas aeruginosa, 16 Staphylococcus aureus, and 16 Candida) and 41 uninfected samples were collected as the experimental group and the control group, respectively. The results revealed a very high VAP identification rate at 94.06% for identifying healthy and infected patients. A 100% accuracy to identify the microorganisms of Klebsiella, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida from VAP infected patients was achieved. This chip only consumes 1.27 mW at a 0.5 V supply voltage. This work provides a promising solution for the long-term unresolved rapid VAP diagnostic problem.

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings.

OBJECTIVE: Around 1% of the world's population is affected by epilepsy, and nearly 25% of patients cannot be treated effectively by available therapies. The presence of closed-loop seizure-triggered stimulation provides a promising solution for these patients. Realization of fast, accurate, and energy-efficient seizure detection is the key to such implants. In this study, we propose a two-stage on-line seizure detection algorithm with low-energy consumption for temporal lobe epilepsy (TLE). APPROACH: Multi-channel signals are processed through independent component analysis and the most representative independent component (IC) is automatically selected to eliminate artifacts. Seizure-like intracranial electroencephalogram (iEEG) segments are fast detected in the first stage of the proposed method and these seizures are confirmed in the second stage. The conditional activation of the second-stage signal processing reduces the computational effort, and hence energy, since most of the non-seizure events are filtered out in the first stage. MAIN RESULTS: Long-term iEEG recordings of 11 patients who suffered from TLE were analyzed via leave-one-out cross validation. The proposed method has a detection accuracy of 95.24%, a false alarm rate of 0.09/h, and an average detection delay time of 9.2 s. For the six patients with mesial TLE, a detection accuracy of 100.0%, a false alarm rate of 0.06/h, and an average detection delay time of 4.8 s can be achieved. The hierarchical approach provides a 90% energy reduction, yielding effective and energy-efficient implementation for real-time epileptic seizure detection. SIGNIFICANCE: An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.