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Low-Power System for Detection of Symptomatic
Patterns in Audio Biological Signals
Abstract:
In this paper, we present a low-power, efficacious, and scalable system for the detection of
symptomatic patterns in biological audio signals. The digital audio recordings of various
symptoms, such as cough, sneeze, and so on, are spectrally analyzed using a discrete wavelet
transform. Subsequently, we use simple mathematical metrics, such as energy, quasi-average,
and coastline parameter for various wavelet coefficients of interest depending on the type of
pattern to be detected. Furthermore, a multi-frequency spectrum-based analysis is applied to
distinguish between signals, such as cough and sneeze, which have a similar frequency response
and, hence, occur in common wavelet coefficients. Algorithm-circuit codesign methodology is
utilized in order to optimize the system at algorithm and circuit levels of design abstraction. This
helps in implementing a low-power system as well as maintaining the efficacy of detection. The
system is scalable in terms of user specificity as well as the type of signal to be analyzed for an
audio symptomatic pattern. We utilize multiplierless implementation circuit strategies and the
algorithmic modification of multi spectrum computation to implement low power system in the
65-nm bulk Si technology. It is observed that the pattern detection system achieves about 90%
correct classification of five types of audio health symptoms. We also scale the supply voltage
due to lower frequency of operation and report a total power consumption of ∼184 µW at 700
mV supply. The proposed architecture of this paper analysis the logic size, area and power
consumption using Xilinx 14.2.
Existing System:
In the past decade, rapid advancements in the development of low-power design methodologies
have resulted in feasible designs for various wearable and implantable medical systems.
Numerous wearable health monitoring systems have been proposed in order to deliver early
warning of an impending health condition. These systems monitor various internal as well as
external parameters related to the human health, such as temperature, heart rate, and so on. Apart
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from these parameters, it is well known that acoustic symptoms, such as cough, sneeze, belching,
and so on, are early markers of serious health issues, such as influenza, diarrhea, and whooping
cough, especially among children. If repetitive occurrence of these symptoms is detected in
advance, it is possible for the patient or the healthcare personnel to commence remedial action
prior to aggravation of the problem. In the literature, most of the developed systems detect a
single acoustic symptom (cough or sneeze). The Kids Health Monitoring System (KiMS)
proposed in uses wearable sensors and acoustic signal processing in order to provide health
monitoring in children. Using the neural network-based processing, the KiMS classifies various
symptoms and activities and, subsequently, transmits the record to a parent or doctor for further
analysis.
Disadvantages:
 High power for monitoring system.
Proposed System:
We describe the proposed algorithm and the methodology used to modify the various
computational tools in order to make it implementable into low-power hardware. In Section II,
we had described the basics and justified the basis for selecting specific computational
techniques used in developing this algorithm. The application of these computations is dependent
on the characteristic property of the symptom to be detected. The algorithm methodology is
shown in Fig. 1. We also describe the details along with the mapping of algorithm to specific
signals as follows.
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Fig. 1. Proposed algorithm/methodology.
We discuss the circuit level techniques that are used to implement the proposed algorithm into a
power efficient hardware.
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Fig. 2. Block diagram of hardware implementation.
Discrete Wavelet Transform Block
The wavelet transform block is the most computationally intensive block in the system and
consumes a significant amount of power. There are various methods available in the literature to
implement the DWT block.
Mathematical Metric Blocks
The block diagrams for the mathematical metric blocks are shown in Fig. 3.
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Fig. 3. Block diagram for (a) energy parameter, (b) CL parameter, and (c) QA.
Threshold Block and Clock Circuitry:
The threshold block consists of registers that are loaded with the prefixed threshold values
corresponding to each individual acoustic pattern to be detected. These threshold values are fixed
in the training phase. Comparators in the threshold blocks are used to compare and raise the
detection flag for each of the symptomatic pattern detected. The clock circuitry is used to
synchronize all the operations in the system.
Advantages:
 efficient low-power health monitoring system
Software implementation:
 Modelsim
 Xilinx ISE

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Low power system for detection of symptomatic patterns in audio biological signals

  • 1. ONLINE IEEE PROJECTS IeeeXpert.com BUY THIS PROJECT FOR 2000 RS –IeeeXpert.com Mobile: +91-9566492473/+91-9042092473 | WhatsApp: 09566492473 Email: contact@ieeexpert.com | Locations: Pondicherry/Chennai Delivering Projects all over India | 100% Output Satisfaction (or) Get Money back Low-Power System for Detection of Symptomatic Patterns in Audio Biological Signals Abstract: In this paper, we present a low-power, efficacious, and scalable system for the detection of symptomatic patterns in biological audio signals. The digital audio recordings of various symptoms, such as cough, sneeze, and so on, are spectrally analyzed using a discrete wavelet transform. Subsequently, we use simple mathematical metrics, such as energy, quasi-average, and coastline parameter for various wavelet coefficients of interest depending on the type of pattern to be detected. Furthermore, a multi-frequency spectrum-based analysis is applied to distinguish between signals, such as cough and sneeze, which have a similar frequency response and, hence, occur in common wavelet coefficients. Algorithm-circuit codesign methodology is utilized in order to optimize the system at algorithm and circuit levels of design abstraction. This helps in implementing a low-power system as well as maintaining the efficacy of detection. The system is scalable in terms of user specificity as well as the type of signal to be analyzed for an audio symptomatic pattern. We utilize multiplierless implementation circuit strategies and the algorithmic modification of multi spectrum computation to implement low power system in the 65-nm bulk Si technology. It is observed that the pattern detection system achieves about 90% correct classification of five types of audio health symptoms. We also scale the supply voltage due to lower frequency of operation and report a total power consumption of ∼184 µW at 700 mV supply. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2. Existing System: In the past decade, rapid advancements in the development of low-power design methodologies have resulted in feasible designs for various wearable and implantable medical systems. Numerous wearable health monitoring systems have been proposed in order to deliver early warning of an impending health condition. These systems monitor various internal as well as external parameters related to the human health, such as temperature, heart rate, and so on. Apart
  • 2. ONLINE IEEE PROJECTS IeeeXpert.com BUY THIS PROJECT FOR 2000 RS –IeeeXpert.com Mobile: +91-9566492473/+91-9042092473 | WhatsApp: 09566492473 Email: contact@ieeexpert.com | Locations: Pondicherry/Chennai Delivering Projects all over India | 100% Output Satisfaction (or) Get Money back from these parameters, it is well known that acoustic symptoms, such as cough, sneeze, belching, and so on, are early markers of serious health issues, such as influenza, diarrhea, and whooping cough, especially among children. If repetitive occurrence of these symptoms is detected in advance, it is possible for the patient or the healthcare personnel to commence remedial action prior to aggravation of the problem. In the literature, most of the developed systems detect a single acoustic symptom (cough or sneeze). The Kids Health Monitoring System (KiMS) proposed in uses wearable sensors and acoustic signal processing in order to provide health monitoring in children. Using the neural network-based processing, the KiMS classifies various symptoms and activities and, subsequently, transmits the record to a parent or doctor for further analysis. Disadvantages:  High power for monitoring system. Proposed System: We describe the proposed algorithm and the methodology used to modify the various computational tools in order to make it implementable into low-power hardware. In Section II, we had described the basics and justified the basis for selecting specific computational techniques used in developing this algorithm. The application of these computations is dependent on the characteristic property of the symptom to be detected. The algorithm methodology is shown in Fig. 1. We also describe the details along with the mapping of algorithm to specific signals as follows.
  • 3. ONLINE IEEE PROJECTS IeeeXpert.com BUY THIS PROJECT FOR 2000 RS –IeeeXpert.com Mobile: +91-9566492473/+91-9042092473 | WhatsApp: 09566492473 Email: contact@ieeexpert.com | Locations: Pondicherry/Chennai Delivering Projects all over India | 100% Output Satisfaction (or) Get Money back Fig. 1. Proposed algorithm/methodology. We discuss the circuit level techniques that are used to implement the proposed algorithm into a power efficient hardware.
  • 4. ONLINE IEEE PROJECTS IeeeXpert.com BUY THIS PROJECT FOR 2000 RS –IeeeXpert.com Mobile: +91-9566492473/+91-9042092473 | WhatsApp: 09566492473 Email: contact@ieeexpert.com | Locations: Pondicherry/Chennai Delivering Projects all over India | 100% Output Satisfaction (or) Get Money back Fig. 2. Block diagram of hardware implementation. Discrete Wavelet Transform Block The wavelet transform block is the most computationally intensive block in the system and consumes a significant amount of power. There are various methods available in the literature to implement the DWT block. Mathematical Metric Blocks The block diagrams for the mathematical metric blocks are shown in Fig. 3.
  • 5. ONLINE IEEE PROJECTS IeeeXpert.com BUY THIS PROJECT FOR 2000 RS –IeeeXpert.com Mobile: +91-9566492473/+91-9042092473 | WhatsApp: 09566492473 Email: contact@ieeexpert.com | Locations: Pondicherry/Chennai Delivering Projects all over India | 100% Output Satisfaction (or) Get Money back Fig. 3. Block diagram for (a) energy parameter, (b) CL parameter, and (c) QA. Threshold Block and Clock Circuitry: The threshold block consists of registers that are loaded with the prefixed threshold values corresponding to each individual acoustic pattern to be detected. These threshold values are fixed in the training phase. Comparators in the threshold blocks are used to compare and raise the detection flag for each of the symptomatic pattern detected. The clock circuitry is used to synchronize all the operations in the system. Advantages:  efficient low-power health monitoring system Software implementation:  Modelsim  Xilinx ISE