Lei-Chun Chou

A Study of the Relationship between Two Musical Rhythm Characteristics and Heart Rate Variability (HRV)

Heart rate variability (HRV) is a measure of variations in the heart rate. Over the last 25 years, HRV analysis has became more and more popular as a non-invasive research and clinical tool for indirectly investigating both cardiac and autonomic nervous system (ANS) function in both health and disease area. How the musical rhythmic characteristics, tempo and complexity, affect the performance of HRV is studied in this work. By understanding the relationship between music and the function of ANS, we can improve our life and health by music - non-invasively and simply.

Through-silicon-via-based double-side integrated microsystem for neural sensing applications

This paper presents a Through-Silicon-Via (TSV) based double-side integrated microsystem for brain neural sensing applications. Figure 6.3.1 shows the structure of the double-side integrated microsystem. MEMS neural microprobe array and low-power CMOS readout circuit are fabricated on two sides of the same silicon substrate, and TSVs are used to form a low impedance interconnection between the microprobe and CMOS circuitry, thus providing the shortest signal transmission distance from sensors to circuits. The low parasitic impedance of TSV minimizes transmission loss and noise. The overall chip is 5x5mm2, 350μm in thickness including 150μm probe height and 200μm TSV height, respectively. A total of 480 microprobes is divided into 4x4 sensing areas, forming 16channels. 16 TSV arrays are used to connect the microprobe outputs to 16 readout circuits fabricated on the opposite side of the silicon substrate. The proposed structure allows stacking of other CMOS chips onto the circuit side by TSV 3D IC technique.

2.5D Heterogeneously Integrated Microsystem for High-Density Neural Sensing Applications

Heterogeneously integrated and miniaturized neural sensing microsystems are crucial for brain function investigation. In this paper, a 2.5D heterogeneously integrated bio-sensing microsystem with μ-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications. This microsystem is composed of μ-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 × 24 μ-probe array with embedded TSVs is fabricated on a 5×5 mm2 chip and bonded on the back side of the interposer. Thus, each channel contains 6 × 6 μ-probes with embedded TSVs. Additionally, the 4 dies are bonded on the front side of the interposer and designed for biopotential acquisition, feature extraction and classification via low-power analog front-end (AFE) circuits, area-power-efficient analog-to-digital converters (ADCs), configurable discrete wavelet transforms (DWTs), filters, and a MCU. An on-interposer bus (μ-SPI) is designed for transferring data on the interposer. Finally, the successful in-vivo test demonstrated the proposed 2.5D heterogeneously integrated bio-sensing microsystem. The overall power of this microsystem is only 676.3 μW for 16-channel neural sensing.

18.6 2.5D heterogeneously integrated bio-sensing microsystem for multi-channel neural-sensing applications

Heterogeneously integrated and miniaturized neural sensing microsystems for accurately capturing and classifying signals are crucial for brain function investigation and neural prostheses realization [1]. Many neural sensing microsystems have been proposed to provide small form-factor and biocompatible properties, including stacked multichip [2, 3], microsystem with separated neural sensors [4], monolithic packaged microsystem [5] and through-silicon-via (TSV) based double-side integrated microsystem [6]. These heterogeneous biomedical devices are composed of sensors and CMOS circuits for biopotential acquisition, signal processing and transmission. However, the weak signals detected from sensors in [2-5] have to pass through a string of interconnections to the CMOS circuits by wire bonding. In view of this, TSV-based double-side integration [6] uses TSV arrays to transfer the weak signals from μ-probe arrays to CMOS circuits for reducing noises. Nevertheless, the double-side integration requires preserving large area for separate μ-probe arrays and TSV arrays, and the TSV fabrication process may induce damage on CMOS circuits.

The relationship between music processing and electrocardiogram (ECG) in vegetative state (VS)

Previous literature has suggested that consciousness of patients in vegetative state (VS) may be enhanced via music listening; however, studies empirically documenting the link among music, vegetative state, and electrocardiogram (ECG) are scant. Therefore, the current article attempts to explore how music improves the VS patients condition with the assessment of ECG recordings. The present work involved a follow-up of case report, consisting of forty-two 150-min sessions, whose duration is longer than other VS studies. The quantitative analysis of the data was conducted through C programming language, HRV Analysis, EXCEL and MATLAB package software in order to find a specific pattern of HRV. Results of the study showed a medium level of consistence between the two variables of music and ECG. To conclude, this publication may be of importance in presenting some specific pattern of the ECG diagram while letting VS patients listen to the music.

A TSV-Based Bio-Signal Package With

Bio-signal probes providing stable observation with high quality signals are crucial for understanding how the brain works and how the neural signal transmits. Due to the weak and noisy characteristics of bio-signals, the connected interconnect length between the sensor and CMOS has significant impact on the bio-signal quality. In addition, long interconnections with wire bonding technique introduce noises and lead to bulky packaged systems. This letter presents an implantable through-silicon via (TSV) technology to connect sensors and CMOS devices located on the opposite sides of the chip for brain neural sensing applications. With the elimination of traditional wire bonding and packaging technologies, the quality of bio-signal can be greatly improved.

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Heart rate variability (HRV) is a measure of variation in the heart rate. Over the last 25 years, HRV analysis has became utilized more often as a non-invasive research and clinical tool for indirectly investigating both cardiac and autonomic nervous system (ANS) functions in health and disease. In our experiment, 22 healthy subjects and four testing rhythm patterns are studied. How the three musical-rhythmic characteristics of tempo, complexity and drum sample affect the performance of HRV is discussed and a computational model with four factors is proposed. This relationship can help us to improve our life and health by music.

-Probe Array

Heart rate variability (HRV) is a measure of variations in the heart rate. Over the last 25 years, HRV analysis has became popular as a non-invasive research and clinical tool for indirectly investigating both cardiac and autonomic nervous system (ANS) function in both health and disease area. How the musical rhythmic characteristics, tempo and complexity, affect the performance of HRV is studied in this work. By understanding the relationship between music and the function of ANS, the novel biomedical entertainment platform is proposed and used for relaxation.

A computational model of the relationship between musical rhythm and heart rhythm

A JPEG XR chip for HD-Photo is implemented with 25 mm2 area in TSMC 0.18 um CMOS 1P6M technology at 100 MHz. According to the simulation results, the 4:4:4 1920x1080 HD-Photo 20 frames/sec can be encoded smoothly.

A biomedical entertainment platform design based on musical rhythm characteristics and heart rate variability (HRV)

Micro Electro Mechanical System (MEMS) technology is used in a bio-electrode for measuring electrocorticography (ECoG) signals in Sprague Dawley (SD) rats. The electrical signal in the rat brain is evoked by auditory stimulation and can be detected with newly developed bio-electrode sensors. A back-end system can record signals from eighteen positions. One signal was used as the ground signal, and one was used as the reference signal. The average results for 100 samples revealed the relationship between temporal resolution and spatial resolution of sixteen positions in the brain. The bioelectrode was fabricated with two parylene layers (10um, 1um) to cover the platinum metal wires. Oxygen plasma is used to etch the bioelectrode and to define a stable size and shape in production of batches of electrodes. To simplify implantation, the bioelectrode was designed to fit the curvature of SD rat head bones, and a special connector was used to transmit signals to the back-end system, which successfully recorded Auditory Evoked Potentials (AEP) signals after implant surgery.