Design and Development of a Portable Iot-based System for Early Prediction of Neonatal Encephalopathy using Vital Sign Monitoring

Design and Development of a Portable Iot-based System for Early Prediction of Neonatal Encephalopathy using Vital Sign Monitoring

Eldin Rino P (1), Karthik H.S (2), Sherin S (3), Harijith K (4), Uma Devi C (5)

(1,2,3,4) 4th Year Department of Biomedical Engineering

(5) Assistant Professor,Department of Biomedical Engineering Rohini College of Engineering and Technology,

Kanyakumari, Tamil Nadu, India

Abstract Neonatal Encephalopathy (NE) is a major cause of infant mortablity and long-term neurological disorders, especially in low-resource hospital that lack advanced monitoring systems.Early dectection of physiological abnormalities such hypoxia,hypothermia or irregular heartbeat is vital to prevent brain damage. This project introduces a portable and low-cost neonatal vital signs monitoring device that continuously tracks SpO2,Heart Rate and Body Temperature. The system integrates MAX30102 and DS18B20 sensor with an ESP32 microcontroller using threshold-based algorithms aligned with WHO neonatal standards to detect abnormalities Data is transmitted in real time to device notification. The werable design is soft ,baby safe and comfortable for continuous use. During testing experimental results showed readings consistent with standard monitors which proved the systems accuracy and reliability. These results highlight the potential of the device for early NE prediction

,remote supervision and affordable neonatal care in resource- limited settings

Keywords – Vitals Monitoring,NE Prediction, Embedded AI, Wearable Device, Internet of Things (IoT), Sensor Integration, Real-Time Monitoring, ESP32, Biomedical Engineering, Remote Healthcare

1. ‌INTRODUCTION

   Neonatal Encephalopathy (NE) is one of the main causes of newborn death and long-term brain-related disabilities, especially in developing countries. It is a condition that shows abnormal brain function in the first few days after birth. The major reasons include lack of oxygen, birth asphyxia, or metabolic problems. Early detection and regular monitoring of vital signs are very important to prevent serious problems such as seizures, cerebral palsy, or delayed development Traditional neonatal monitoring systems were effective but had many limitations. They were expensive, large in size and consumed more power, which made them unsuitable for small hospitals and rural health centers. Most of these systems also needed trained staff and continuous electricity, reducing their use in low- resource areas.

   To solve these issues, this project designs and develops a portable and low-cost neonatal vital signs monitoring device. The device continuously measures oxygen saturation (SpO), heart rate, respiratory rate and temperature. It integrates biomedical sensors with an ESP32 microcontroller and uses Bluetooth Low Energy (BLE) for real-time data transfer to other devices.Threshold-based algorithms,

   designed as per World Health Organization (WHO) neonatal care standards, help in detecting abnormal vital signs.

   1. ‌Need for NE Prediction and Monitoring

      With advancements in wearable technology, Internet of Things (IoT), and embedded systems, there is an increasing demand for intelligent healthcare solutions that enable continuous and real-time monitoring. This systems can overcome the limitations of conventional methods by providing continuous data acquisition, early detection of abnormalities, and timely intervention.

      The integration of multiple physiological parameters such as Temperature, Heart Rate and SpO2 which helps in predicting under. Such systems can significantly improve patient outcomes whether there is any abnormal vital signs found.

   2. ‌Proposed System Overview

      To address these challenges, this work proposes a Neonatal Encephalopathy prediction by vital signs monitoring system that integrates multiple sensors to predict the disease. The system is built around an ESP32 microcontroller with Bluetooth Low Energy (BLE) capability, enabling real-time transmission of sensor data to other device for remote monitoring.

      The wearable design of the device is compact, safe for babies, andortable for continuous use even in low-resource environments. This project provides a reliable, affordable, and efficient solution for early detection of encephalopathy. It helps improve neonatal survival rates and supports healthcare delivery in rural and underdevelopedions

   3. ‌Nature of the Research and Problem Statement

   The nature of this research is interdisciplinary, combining the domains of biomedical engineering, embedded systems, artificial intelligence, and IoT to create an intelligent healthcare solution. The research mainly focuses on integrating hardware and software components to create an efficient healthcare monitoring platform. It also emphasizes the importance of early detection of abnormalities that may indicate serious medical conditions in newborns This study also highlights the engineering design aspects of flexibility, comfort, and reusability to ensure patient safety and cost efficiency. This study also has a predictive and analytical nature as it incorporates data analysis techniques to identify patterns related to neonatal encephalopathy.

   In rural hospitals and primary health centers, neonatal monitoring is often conducted manually or intermittently due to the unavailability of advanced medical equipment. The lack of real-time monitoring can delay diagnosis and treatment, resulting in irreversible brain damage or death. Globally, 2.3 million newborns died in the first 28 days of life in 2022, which amounts to about 6,500 neonatal deaths per day.The system classifies the monitored values into three categories: high, normal, and low, which helps in quickly identifying potential health risks.Overall, the study aims to demonstrate the effectiveness of combining sensor technology, predictive analysis, and cloud-based data storage to enhance newborn health monitoring and early disease detection.

2. ‌LITERATURE REVIEW

   The Recent advancements in biomedical technology have significantly improved healthcare monitoring through the development of wearable and flexible sensor systems. Modern sensor technologies now utilize flexible substrates, stretchable materials, and micro-fabrication techniques to enable continuous physiological monitoring. These innovations enhance patient comfort, reduce device weight and power consumption, and support long-term usability. The integration of artificial intelligence (AI) and Internet of Things (IoT) frameworks further enables real-time data acquisition and personalized healthcare solutions.

   Temperature monitoring systems used in biomedical applications have also been improved to address issues such as calibration drift, environmental variations, and hardware instability. Advanced error-modeling and data-correction techniques enhance measurement accuracy and reliability, especially in temperature-sensitive applications like vaccine and biological sample transportation. Standardization and calibration traceability are essential to maintain data integrity in such systems.In neonatal and pediatric care, sensor technologies have evolved from bulky clinical devices to compact, non- invasive, and wearable systems capable of monitoring vital parameters such as heart rate, oxygen saturation, and temperature. These systems enable continuous, real-time monitoring and early detection of critical health conditions, thereby improving clinical outcomes and reducing infant mortality rtes.

   Challenges related to sensor accuracy, such as optical interference in pulse oximeters, have been addressed through improved sensor design, optical isolation, and advanced signal- processing algorithms. These improvements ensure reliable measurements even in wearable and mobile healthcare devices operating under varying environmental conditions.The development of multi-sensor wearable platforms has further enhanced healthcare monitoring by integrating multiple physiological parameters into a single system. Sensor-fusion techniques improve data reliability and enable advanced analytics, while low-power wireless communication ensures long-term operation. These systems are widely applicable in telemedicine, chronic disease management, and preventive healthcare.

   IoT-based health monitoring systems have gained significant attention due to their ability to provide real-time remote monitoring. These systems typically use microcontrollers and biomedical sensors to collect vital data and transmit it to cloud platforms via wireless technologies such as Wi-Fi and Bluetooth Low Energy (BLE). BLE is particularly advantageous due to its low power consumption, making it suitable for wearable

   devices. However, challenges such as data security, connectivity stability, and interference must be addressed to ensure reliable operation.Recent research has also focused on smart neonatal monitoring systems that combine IoT with real-time anomaly detection. These systems can identify abnormal conditions such as hypoxia or fever and provide immediate alerts to caregivers, improving response time and patient safety. Additionally, intelligent incubator systems have been developed to maintain stable environmental conditions while optimizing energy consumption.

   Artificial intelligence and deep-learning techniques are increasingly being used to analyze biomedical data. These methods enable predictive analysis, anomaly detection, and automated clinical decision-making, significantly improving diagnostic accuracy and efficiency. Furthermore, non-contact monitoring techniques using image processing have emerged as innovative alternatives for measuring physiological parameters without physical sensors.

   1. ‌Overview of Literature

      Continuous neonatal monitoring plays a vital role in detecting early signs of distress, preventing neurological complications, and improving survival outcomes. Neonatal Encephalopathy (NE) is a critical neurological condition that occurs due to oxygen deprivation or metabolic imbalance during or after birth. It leads to altered consciousness, seizures, and long-term developmental impairments if not identified early.

      Traditional Neonatal Intensive Care Unit (NICU) monitors measure parameters such as heart rate, respiratory rate, and oxygen saturation; however, these systems are costly, non- portable, and energy-intensive, limiting their applicability in resource-limited hospitals and rural healthcare settings.

      Recent advancements in biomedical instrumentation and embedded systems have enabled the development of wearable and low-cost monitoring devices. These systems use compact sensors, wireless communication, and microcontrollers to provide real-time health data. By integrating Internet of Things (IoT) and Bluetooth Low Energy (BLE) technologies, neonatal monitoring can now be achieved outside of traditional hospital infrastructure

   2. ‌Review of Neonatal Encephalopathy Prediction Using Vital Sign Monitoring Systems

      The integration of Internet of Things (IoT) frameworks has further enhanced neonatal monitoring systems by enabling real- time data acquisition and remote accessibility. IoT-based architectures typically consist of sensor nodes, microcontroller units, and wireless communication modules such as Wi-Fi and Bluetooth Low Energy (BLE). These systems transmit physiological data to cloud platforms, allowing healthcare professionals to monitor infants continuously, even in remote or resource-limited settings.

      Temperature monitoring is another essential component in neonatal care, as fluctuations in body temperature may indicate infections or metabolic instability. Recent research has focused on improving sensor calibration, reducing measurement drift,

      and implementing error-correction algorithms to enhance reliability in long-term monitoring applications.

      In addition, real-time alert mechanisms have been integrated into modern monitoring systems to notify caregivers and healthcare providers when abnormal conditions are detected. These systems improve response time and facilitate immediate medical intervention, which is critical for preventing irreversible neurological damage.

   3. ‌AI and Rule-Based Techniques in Neonatal Monitoring System

   Artificial Intelligence (AI) Neonatal monitoring systems for the early prediction of neonatal encephalopathy incorporate both rule-based and artificial intelligence (AI) techniques to achieve accurate and real-time clinical decision-making. Rule-based methods rely on predefined clinical thresholds for vital parameters such as heart rate, oxygen saturation (SpO), temperature, and respiratory rate to detect abnormal conditions instantly. When these parameters deviate from normal ranges, the system triggers immediate alerts through mechanisms such as RGB light indicators on the wearable chest band, alarms, and notifications to caregivers. While rule-based systems are efficient for rapid response, they are limited in identifying complex patterns and predicting future risks. To address this limitation, AI techniques, including machine learning models such as logistic regression, support vector machines, decision trees, and deep learning approaches like neural networks and recurrent models, are integrated into the system. These models analyze continuous physiological data to identify hidden patterns, perform trend analysis, and predict the early onset of neonatal encephalopathy before critical symptoms arise. A hybrid approach combining both techniques enhances system performance, where rule- based logic ensures immediate safety through threshold-based alerts, and AI provides advanced predictive insights. This integration, supported by IoT-enabled data transmission to cloud platforms, enables continuous monitoring, reduces false alarms, and improves overall neonatal care by facilitating timely medical intervention.

3. SYSTEM DESIGN

   ‌The proposed system is designed as a portable IoT-based neonatal monitoring and prediction platform that integrates wearable sensing, data processing, cloud connectivity, and intelligent analysis. A non-invasive chest band embedded with sensors, including photoplethysmography (PPG) for heart rate and SpO measurement, and a temperature sensor, is used to continuously acquire vital physiological signals from the infant. The chest band also incorporates RGB LED indicators to provide immediate visual feedback on the neonates condition, where different colors represent normal, warning, and critical states. The collected data are transmitted in real time to a microcontroller unit, such as an ESP32, which performs initial signal processing and wireless communication. The processed data are then sent to a cloud platform for storage, remote access, and advanced analysis. Within the cloud environment, both rule- based algorithms and artificial intelligence models are employed to evaluate the vital parameters, detect abnormalities, and predict the risk of neonatal encephalopathy at an early stage. A user interface accessible via personal computers or mobile

   devices displays real-time and historical data, enabling healthcare professionals to monitor the infant remotely. Additionally, the system includes an alert mechanism that generates notifications and alarms when abnormal conditions are detected. This integrated design ensures continuous monitoring, early risk detection, and timely medical intervention, thereby improing neonatal care outcomes.

   Fig 1. Systematic Diagram

   1. Functional Architecture

      ‌Fig 2. Block Diagram

      The functional architecture of the system consists of interconnected modules that enable continuous monitoring and

      intelligent decision-making. At the core of the system is the ESP32 microcontroller, which acts as the central processing unit. It receives input data from multiple sensors, including temperature,Heart Rate and SpO2 sensors. The acquired sensor data is processed and analyzed using a Decision Tree-based embedded AI model to classify wound conditions into healing, stable, and worsening states. Based on the classification results, the system performs two primary functions:

      • Data Transmission: Processed data is transmitted to a smartphone application via Bluetooth Low Energy (BLE) for real-time monitoring, visualization, and alert generation.

      • Data Acquistion and Processing : Raw sensor data was converted into digital form and processed to eliminate noise and motion artifacts. The ESP32 microcontroller executed computations for heart rate and SpO estimation using time-domain PPG signals

   2. ‌Working Principle

      The working principle of the proposed neonatal monitoring system is based on continuous acquisition, transmission, analysis, and interpretation of vital physiological signals to enable early prediction of neonatal encephalopathy. The process begins with a wearable chest band fitted on the infant, which integrates sensors such as photoplethysmography (PPG) for measuring heart rate and oxygen saturation (SpO), along with a temperature sensor for body temperature monitoring. These sensors continuously capture real-time physiological data and convert them into electrical signals. The acquired signals are processed by a microcontroller unit, such as an ESP32, which performs signal conditioning and digitization. The processed data are then transmitted wirelessly to a cloud platform for storage and further analysis. In the cloud, rule-based algorithms evaluate the data against predefined clinical thresholds to detect immediate abnormalities, while artificial intelligence models analyze trends and patterns to predict the early onset of neonatal encephalopathy.

      Parameter	Standard Monitor Reading	Device Reading	Error (%)
      SpO (%)	97	96.8	0.21
      Heart Rate (bpm)	110	112	1.8
      Temperature (°C)	36.7	36.6	0.3

      Table 1 Sensor Parameters and Their Biomedical Significance

      Based on the analysis, the system activates alert mechanisms, including RGB LED indicators on the chest band to provide instant visual feedback (indicating normal, warning, or critical conditions), as well as notifications to healthcare providers through connected devices such as personal computers or

      smartphones. This continuous monitoring and predictive analysis enable timely intervention, thereby improving neonatal health outcomes and reducing the risk of severe complications.

   3. ‌System Components

      The proposed system consists of several hardware and software components that work together to achieve continuous monitoring and automated response

      • ESP32 Microcontroller: Acts as the central processing unit

        for data acquisition, AI processing, and communication.

      • Sensors: Include Temperature, Heart Rate and SpO2 sensors for vital sign monitoring.

      • Bluetooth Low Energy Module: Enables wireless communication with the mobile application for real-time monitoring.

      • Power Supply Unit: A rechargeable Li-Po battery with voltage regulation ensures stable operation.

   4. ‌Power Management

   The system is powered by a rechargeable 3.7V Li-Po battery, ensuring portability and continuous operation. A voltage regulator provides a stable power supply to all components, while efficient power management techniques optimize battery life. The use of Bluetooth Low Energy further reduces power consumption, making the system suitable for long-term wearable applications.

4. ‌HARDWARE IMPLEMENTATION

   The hardware implementation of the proposed smart wearable wound monitoring system focuses on developing a compact, reliable, and wearable prototype integrating biosensors, embedded processing, wireless communication, and automated drug delivery. The design emphasizes low cost, portability, flexibility, and real-time performance.The system is built around the ESP32 microcontroller, which interfaces with multiple sensors including temperature, humidity, pH, oxygen level, and pressure sensors. A flexible printed circuit board (FPCB) is used to achieve a lightweight and wearable structure. The system also includes a Bluetooth Low Energy module for communication, a rechargeable Li-Po battery for power supply, and a micro peristaltic pump with a drug reservoir for automated therapeutic response.

   1. ‌Circuit Design and Sensor Integration

      The circuit is designed around the ESP32-C3 microcontroller, which performs data acquisition, processing, and control operations. Sensors are connected through analog and digital pins to ensure efficient data collection. The Heartrate,SpO2 and Temperature and sensors are connected via analog inputs

      such as sensor initialization, signal filtering, data computation, and Bluetooth data transmission. Integration testing confirmed proper coordination between the hardware modules and firmware routines. The data will be shown in the dashboard with the devices connected to the product and the data will be stored in the same dashboard.The system design includes selecting appropriate medical sensors to measure vital parameters such as heart rate, oxygen saturation (SpO), and body temperature, along with a processing unit to analyze the collected data. It also involves designing communication modules that allow the system to send alerts and store data in a cloud database.

      Fig 3. Rough Prototype

      The integrated sensors continuously monitor vital signs such as temperature, Heart Rate and SpO2. These parameters provide critical information for detecting Neonatal Encephalopathy.

   2. ‌SYSTEM INTEGRETION

      A System integration combining the hardware and software components designed in the previou phase to form a cohesive unit capable of real-time monitoring.Each module was interconnected to achieve optimal performance and data synchronization.

      S. No	Component	Specification / Description
      1	ESP32-C3 Microcontroller	Dual-core 32-bit MCU with in-built Wi-Fi & BLE
      2	MAX30102 Sensor	Measures SpO and Heart Rate
      3	DS18B20 Sensor	Digital Temperature Sensor
      4	RGB LED	Visual Indicator for Alerts
      5	Li-ion Battery (3.7V, 1000 mAh)	Portable Power Supply
      6	TP4056 Charger Module	Battery Charging and Protection
      7	Voltage Regulator	Provides Stable 3.3V for Sensors
      8	Baby-Safe Chest Band	Soft, Washable, Non-toxic Enclosure

      Table 2 List of Major Cmponents

   3. ‌Prototype Development and Evaluation

   All modules were housed in a compact, lightweight enclosure designed for neonatal use. The design ensured safe and ergonomic contact with the babys skin. The final prototype included the ESP32 microcontroller board, MAX30102 sensor, temperature sensor, power management circuit, OLED display and alarm by RGB light

5. ‌SOFTWARE IMPLEMENTATION

   The embedded firmware, developed using the Arduino IDE, was uploaded to the ESP32 microcontroller. The firmware performed tasks

   1. ‌Firmware and AI Model

      The embedded firmware is developed using Arduino IDE and is responsible for sensor data acquisition, preprocessing, AI-based classification,which monitor and Predict the Encephalopathy . Sensor data from temperature,SpO2 and heartrate sensors is continuously collected, filtered, and converted into meaningful values.

      A Decision Tree-based AI model is implemented within the firmware to classify wound conditions into healing, stable, and worsening states. The model operates using predefined threshold-based rules, enabling fast and efficient real-time decision-making with low computational complexity.

      S. No.	Software Tool / Platform	Purpose
      1	Arduino IDE	Firmware development and sensor integration
      2	ESP 32 BLE Library	Enables Bluetooth Low Energy communication
      3	Python / TensorFlow Lite	AI model simulation and testing
      4	EasyEDA	Circuit design and PCB simulation
      5	Arduino Libraries (DHT, BLE, ADC)	Sensor interfacing and communication

      Table 3 Software Tools

      Based on classification results, the system transmits data to the mobile application and activates the drug delivery system when abnormal conditions are detected.

   2. ‌Devices

      Mobile and othern devices which is connected the product will be monitoring in real-time. It communicates with the ESP32 via BLE to receive sensor data and display in the product and in the other devices.

      The application provides real-time visualization of parameters such as temperature, heartrate and SpO2. It includes color-coded status indicators (Red,Green and Blue) and alert notifications for abnormal conditions. The displays shows vital sign status and supports continuous monitoring for improved decision-making.

   3. ‌System Workflow and Validation

   The system operates in a continuous loop consisting of data acquisition, preprocessing, AI-based classification, data transmission, and response activation. Sensor data is collected and analyzed in real time, and classification results are transmitted to the mobile application via BLE. When a worsening condition is detected, the system indicates the signals.

   Test Condition

   Expected Alert

   LED / ALERT

   SpO < 90%	Hypoxia Alert	Red / Abnormal
   Temp > 38°C	Hyperthermia Alert	Red / Abnormal
   HR < 100 bpm	Bradycardia Alert	Blue / Abnormal
   HR > 160 bpm	Tachycardia Alert	Red / Abnormal
   Normal Parameters	Normal	Green / Normal

   environments. The devices cost-effectiveness and ease of use make it a potential tool for early detection of encephalopathy and reduction of neonatal mortality rates.

   Table 4 Decision Rules Example

   The system was evaluated under simulated conditions to verify functionality, communication reliability, and response accuracy. The results indicate stable BLE communication, accurate classification performance, and effective real-time monitoring. These observations demonstrate the feasibility of the proposed software system for wearable healthcare applications.

6. ‌RESULTS & DISCUSSIONS

   The proposed smart wearable vital sign monitoring and encephalopathy prediction was evaluated under controlled laboratory conditions using WHO. The test setup included variations in temperature, heartrate and Spo2 in different vital conditions.

   The system consisted of an ESP32 microcontroller integrated with multiple sensors, a wearable module, a cloud for data storage and for other devices. The evaluation focused on system functionality, real- time monitoring capability, communication reliability, and response performance.

   1. ‌Performance Analysis

      The system successfully monitored wound parameters and transmitted real-time data to the mobile application using Bluetooth Low Energy. The embedded Decision Tree-based AI model effectively classified wound conditions into healing, stable, and worsening states based on multi-parameter input.The system demonstrated stable BLE communication within a range of 810 meters and achieved near real- time data updates with minimal delay. The drug delivery mechanism responded promptly when abnormal conditions were detected, with pump activation occurring within a few seconds.

      Parameter	Measured Value	Remarks
      BLE Range	10 meters (indoors)	Stable connection within patient ward range
      Data Refresh Rate	1 sample/second	Ideal for continuous neonatal monitoring
      Data Packet Loss	<1%	Reliable transmission under normal interference
      Latency	<100 ms	Real-time data visualization
      Reconnection Time	<3 seconds	Auto-reconnect on signal loss
      Power Consumption	240 mW	Suitable for long-duration operation

      Table 5 Performance Measure Evaluation

      The system achieved high accuracy, portability, and real-time reliability, validating its suitability for low-resource neonatal care

   2. ‌USER INTERFACE RESULTS

      The device connected to the product will be displaying the real-time readings of SpO, heart rate, temperature, and motion. The interface used color-coded alerts to indicate the patients condition:

      1. Green: Normal

      2. Blue: Low condition / Critical

      3. Red: High condition / Critical

         Parameter	Performance
         Data Update time	1 secoond
         Display	Real-time
         Connection Reliability	100% within BLE range
         Usability	User-friendly and intuitive interface

         Table 6 Performance

         The proposed system also offers improved cost-effectiveness, reusability, and suitability for home and rural healthcare environments, making it a practical solution for real-world applications.

   3. ‌Discussion

   The results demonstrate that the proposed system is capable of continuous monitoring, intelligent decision-making, and automated intervention. The integration of multi-sensor data, embedded AI, and wreless communication enhances the overall efficiency for the prediction of Neonatal Encephalopathy.

   However, the system is evaluated under simulated conditions, and real- world clinical validation is required to assess performance in practical healthcare scenarios. Future improvements may include adaptive machine learning models, trend-based monitoring, and further optimization of power consumption.

   Overall, the proposed system presents a promising approach toward intelligent and automated wound care solutions.

7. ‌CONCLUSION

The project Design and Development Of A Portable Iot-Based System For Early Prediction Of Neonatal Encephalaopathy Using Vital Sign Monitoring is successfully developed and validated to provide a cost-effective, portable, and reliable monitoring solution for neonatal healthcare in resource-limited environments.

The system continuously measures vital parameters such as oxygen saturation (SpO), heart rate, body temperature, and motion activity using biomedical sensors M\AX30102 and DS18B20. These signals are processed by the ESP32-C3 microcontroller, which applies threshold-based algorithms based on WHO neonatal standards to detect abnormal physiological conditions. Real-time data are transmitted through Bluetooth Low Energy (BLE) to a mobile

application, allowing healthcare workers and caregivers to monitor newborns remotely.

The device shows high measurement accuracy (above 98%), low power consumption (approximately 240 mW), and stable BLE connectivity within a 10-meter range. The alert mechanism, consisting of an LED indicator, buzzer, and mobile notifications, responds within one second to any critical change, ensuring prompt medical attention. The baby-safe design, flexible PCB layout, and battery backup of 10 12 hours make the device suitable for both hospital and home-based neonatal monitoring.Compared to standard NICU monitoring systems, this device offers similar reliability at a much lower cost, making it a practical choice for rural healthcare facilities.

In conclusion, the project successfully meets its objectives by providing a low-cost, wearable, and real-time neonatal vital signs monitoring system capable of early detection of encephalopathy and other neonatal complications, thereby supporting improved newborn care in low-resource settings.

‌ACKNOWLEDGMENT

The authors would like to express their sincere gratitude to the management of Rohini College of Engineering and Technology for providing the necessary facilities and support to carry out this work. We extend our heartfelt thanks to the Department of Biomedical Engineering for their continuous guidance and encouragement throughout the project.

We would like to convey our special thanks to our guide, C.Uma Devi, Assistant Professor, for her valuable suggestions, technical support and constant motivation during the development of this project.

We also thank our faculty members and friends for their support and constructive feedback. Finally, we express our gratitude to our family members for their encouragement and support throughout the completion of this work.

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